{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import cv2\n",
    "import numpy as np\n",
    "from PIL import Image\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib.patches as patches\n",
    "import math\n",
    "%matplotlib inline\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "seq_name = \"Campfire\"\n",
    "QP = \"QP22\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "def frame_processing(filename,currframe = 0):\n",
    "    f = open(\"D:/frame/\"+filename+\"/\"+filename+'%d'%currframe+'.txt', \"r\")\n",
    "    rowlist = []\n",
    "    for line in f:\n",
    "        rowpix = line.split(\" \")\n",
    "        results = [int(i) for i in rowpix[:-1]]\n",
    "        rowlist.append(results)\n",
    "    img = np.asarray(rowlist)\n",
    "    img = img /4\n",
    "    return img"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "def frame_padding(frame,CTU_size = 128):\n",
    "    width , height = frame.shape[1],frame.shape[0]\n",
    "    #print(width,height)\n",
    "    width_CTU_nums = math.ceil(width/128)\n",
    "    height_CTU_nums = math.ceil(height/128)\n",
    "    pad_frame = np.zeros((height_CTU_nums*128,width_CTU_nums*128))\n",
    "    for x in range(height):\n",
    "        for y in range(width):\n",
    "            pad_frame[x][y] = frame[x][y]\n",
    "    return pad_frame"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "img = frame_processing(seq_name,0)\n",
    "yuv_img_padding = frame_padding(img)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "origin frame:\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x205efc67940>"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "yuv_img = Image.fromarray(img.astype('uint8')).convert('YCbCr')\n",
    "\n",
    "print(\"origin frame:\")\n",
    "plt.imshow(yuv_img)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "padding frame:\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x20585fd4fd0>"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "print(\"padding frame:\")\n",
    "yuv_img_padding = Image.fromarray(yuv_img_padding.astype('uint8')).convert('YCbCr')\n",
    "plt.imshow(yuv_img_padding)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "def frame_partition(test_seq,unit , img , yuv_img , QP = \"QP\" , currframe = 0):\n",
    "    frame_width , frame_height = img.shape[1],img.shape[0]\n",
    "    CTU_NUM = math.ceil(frame_width/128) * math.ceil(frame_height/128)\n",
    "    frame_partition = np.zeros((frame_height,frame_width))\n",
    "    fig,ax = plt.subplots(1)\n",
    "    ax.imshow(yuv_img)\n",
    "    begin = currframe * CTU_NUM\n",
    "    for i in range(begin,CTU_NUM+begin):\n",
    "        f = open(\"D:/frame/\"+seq_name+\"/\"+\"/\"+QP+\"/\"+unit+'_%d.txt'%(i), \"r\")\n",
    "        for line in f:\n",
    "            row = line.split(\" \")\n",
    "            startx , starty , height , width = int(row[0]) , int(row[1]) , int(row[2]) , int(row[3])\n",
    "            if startx == 0 and starty==0 and height == 0 and width == 0:\n",
    "                continue\n",
    "            rect = patches.Rectangle((startx,starty),width,height,linewidth=1,edgecolor='red',facecolor='none')\n",
    "            ax.add_patch(rect)\n",
    "    print(unit+\" Partition:\")\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CTU Partition:\n"
     ]
    },
    {
     "data": {
      "image/png": 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nE0MuRA24ew28TtCYPIUXtmxJpUuAzYbwTz7ppBR6N3kozhw/N5E8GL+E7HkjTwuW8TF70r140aLkG4BtcUcXFUL0SqlnASzRWu8y4j4JoFNr/Qml1M0AFmit36uUOhHA1QBOBLAcwF1a6+Uu/bZlimPQRVFEX5TKQmLNtNFsmmz/Jtt+GT5MFKKP6dmWSUXqeRFEHaMTEfpdfuXxeVIRvYU2ALinfn4PgI1G/Nd0jX4GYB+l1MtdijgKoq/Zebueeagouin6cEx2AwUUR9KmjlgZG9KOkfE1bKF2Y3RwCmnEJoJ8ZfG/v/mNE2EXJRuC1oi/35S2+fnnoQA89+yzUGhsQ/jM738PhcaWg0BW98O/+IXVH5PvqHXrEmROY+UcwXMZQuA0755Qu4ny+Ywdvlk4nzmlgGRLwaVLvO17QkUbeg3g20qpXyqlLq3HvUxrvQ0A6scD6vGvALDZkN1Sj0uRUupSpdTDSqmHd1Jc/bhz587kOqbySRXHV5F5nE0uJLh0SPGxfvB4m2xIPiRe3t0PkTHTQhq52MYlpuxc95iv8W3Lj81Pl3+8cXHVB9/1aaeemqt+UXjH298elBdaxpfTp++4w1tnQ3wy4+G55vz333efqGvWrFkp33fs2AEA6O3tBQDMmzcPAPDtBx/M5KunpyfI/ujoaLJkcFtbbXX3lpYWUSYpjzo/HclPAMmSxePj44l+k5fT8mXLkiUPWltbAQBjY2Mir0RFh24O1FpvVUodAOA7qA3N/JvWeh+Dp0trvUAp9e8APq61/lE9/iEAN2mdrHKQIfphKvQDiuhjDpmy5EMbumbZL0sHyfv08HSXnFSdbbrLKAMbhep28dnyXSZNVD223bNm3oNQG7Z07rPvmFcnIvTm1eE6SrJqIoZutNZb68cdAP4vgGUAttOQTP24o86+BcCrDPFXAtgaZS/SPwVg//32cyKvqU5TYegmlLivrg9pmp37PrYVIZcO2wc6fu66DxNxj8ocAgzpSdBuTkVJQuC2dM3iYvSajd8vH344FW9+lOWyfFEzCqe+4x1QQGpXqSPXrk3x0jUNzdh6L5LvJGMuQ6yQHbKhKZuHH3ZY0vvM8yNcbkSvlNoTQIvWuq9+/h0AHwFwNIAO42PsQq31TUqptwK4Co2PsXdrrZe5bHBEn8vPSZafSP0+ZFG23jIoBMWhoP1m9wQ56kIOe82mvIg+tDdXln2XXCjid/UsYUmz6SgLje/OiP5lAH6klPoNgJ8D+Het9bcAfALAsUqppwAcW78GgP8A8AyApwH8FYArC9guDe1NB2pmOTRD90Q2iBI6d/HR+Re/8IXgvJs9F1vvxEcTWZcl9MnRMT8WbeTpZyefHzze1uOSwne+/W0RuW994QUo1D7K/uiHP3Tae/Bb3xJ7CWvXrEl+nuIbjfAfpyheWgKBpk/yaZR0NKdicvTOP9xOm2WKlyilf1FQx1RGs0WpbNRls4FA/c0qq8m4BxNtc6r1HMv2Jy8aL0OHC9GbPAhI9+l3PY9FdEwmot+taLLQ/0TYdY2FF/Uh5mGf6IYxlI+jedu1hLBieo6hPYayKUR36LcIut62dasT9ceS0TCJyJ6WJxgaHLTa5VM/wdLJxtfvv1/ke2HLFnFhNIVaj+N3jz6Kb/3nf1rzTGPy5k9UJqInOxyV03HZ0qUZHmWkmbz0UxQdFy9alPDYllZw0ZRH9Lv7GH0zkVqo7rLykLfnMJXLsCxyoUVKz9Mbmqy8NxvRhyLaEJ98usx4WPT+7tFH0d7ejj/84Q84bv168buAS29M3lz5nUH0BakIEmkmTQTajm2UbfE0NvjGN7xhQnoqRciGCvMGny1pbD6mjHz3qKzyjslvaP5D7HF9nR0doj06Dg4MZHz63aOPivpo0TKeRj9KmfJSGQBAtVpFZ2cntm5tTARUaKB4hQaS58siSL0V85qP6SvYx+jNDUj40STiDaXdGtGHoA4XQpgIyovkQtMngkLQFuf1xTWTfAjLFhejM1bHZN/HovbLlg+t9xKiDfUtxqbZmNpkbPXf9ny48uDjtfnH7arpjugVgLe/7W3BaGOiHrKiCIhT2d3nIhTyLaAZ5ZzX7zLvQ1GarEbeh9BtPRcePzoyEl23TXlaloACbenn8s9E/sNDQyJyB8K+P/BrTkIDCoXapuB0TmPxZpxCY3YNH6vn16tWrkyhe9MeIXnSJd0XWh4hTxvzkkH0eanZSGyykV4IFfHRhcRCbMXKSzrKoLIQfV7fml2PQxBlrH0JDYfos6FoBPoWgop9MlJ5TCSidz0HJs+0QfSuyjVVG8iYN+5UzYNJRXyUxq6BuPKJtW8+mGX3sCQ7RaiZ/pn5H+jvdyJ22kCbI+8iKB5IN1AhMtzH7q4uKNSWIH78scdENMxn0vzD3/+9iOKf+f3v8eQTT0AB2LJ5cyr9lve9L3V9zNFHp1C5mUbomwL95UponGQ5vzmPnsb+pU1KpPu0YvnyZAaOORc/lHYLRA80r0EMRTrN0r870ESWQVHkXNR+s3RMhDxvVF1I0qY/BA2XSTHo2GY/j44YFD6RiN6VJ0l22iD617/udVi4YEFu1NMstDRD5ZF5jyb7pZinvrhkiiL2mAbW7P0oNDba5rq4X9Ixj9+SvKsXodCYPw92vWP79tT1o488klpu2CSNrD3zSN8DTN4bb7ghxUN/vRI6P+H440VbUh5p3F2a326i99WrViVx5l+znN91HxQaK66uXLFC0CDTtEb0IUgmRMdko8nJpjxoksfZ0GaIzby9rhhUFqpL4gktn4msf66yz0NF82jzwVYvQhB9LLKWfC2C6PMcESkjldO0Q/SaHWNIAbjwgguS9SfecNBBLyl0nxeVlU3mPdRCfFFSAA58+cu9+eRIiY/j5/ErpgHNm1/TT58vZl643MjwcDDSlnSH+CD5QXED/f1OW3RNf8Zyu2ecfjo2nHJKKu7iiy7K6JIQteTb8evXQyG914JNlusn1E9IndqYtxx1VIqP4s2NSgj92+bRUzyffVOUpnRDzwtaurYFAOjv78eBBx4IAHjd616X0umzS+FNhxzifBB8frn8leyF5MvmLz+X9PN4yb6k18cn8UiNVEw+ffeK0ukec/005LdwwQKnfIhfNn9scb775qtPPJy/aZOzXkk6TOrr67P6EOqbz0eX/mq1atGWJj7CcNqppwKo+d9vvCwA4Nlnn03x3nbrrQCAKy6/XCyTDaecghNPOAFAY8MPvtHHJz7+cW+9o41HuI6hoaEUH20QYm5YQpuFkA4azkn8rPOSbqKlS5ZkdqWaNh9jac9YYOKGboiPH3dnKpoHLh+rL4Zf4vXJ2/wr8x7yF1es3rLvQQwPb7iaVVYu2RDffB86OY/vvttsxXwEDZEN9SdGR6ismg5DN743q0/27LPOwvXXXZerYTrxhBMK2Z9OxMtuIl98IffA9WAqAAv22ceL1CVkOhXuP/nke9EpNKZI8tBfqWBwYAB9vb2ZpQV6urtr+dQaCrUfk0J6GVKajdeVNtDfj86ODnR1dop81KCdcPzxeOuJJ0IBOO7YY6EAHHvMMdbehOT/Cccfnwz/2IaFbXXpLUcdlfwoRUM3NAxDQzS0SQkF4iMdy5ctyyyHYC55YNqjIRzSvXLFigTJm3pDaUoj+rwfY11vQZ+MS8dLlULLLhQdxdpGoH2bP2X5wRF9Xvmi9kN5qOw6Ozowb948bN++Hfvssw/2mjdP7J0Q2Ro73/Mg+cj1cj093d0YHx9HW1sb5u29t7WMY9CwzZ88z7IPUZeByvPKAkDLdED0RLE3hsbVzUpD47Whb8Kp0rBPBVQpkeSXDR1N1TxMJSqzjHg932OPPTA2NoY5c+Y0NqyuB5rG+MKWLRl5M1THxhKdhPp5IFTO7Y+NjiYy/ZUKFGoNPADMnz8fWmsMDw+n7G/csCGlR3qB8B+TuO9nvvOdmXLVjE8iUwd9rF27Zk2C2CVEbSJ8Qt7ST1H0YZZ4KI0Q/GGHHpqKN/Mm+RlKUxrR28boY9B5UVRXBhosQpNtvwwfmi0fguTKRPR59MbmIY993ihKaN1M5zw+JF82opd6Ei5/fAg+xvci+Z5sRG/StED09FY895xzUm+3M04/3fk2k27cZDeWzaSpiJjN+8WRfVn+RiEaSyhCUkNk0+mrf2XUT410WZvj7YMDAwmflH8NuXySsfP6GL6kgwL1DqRy0OPjUGj8vEQ/cw0NDgJIb0ZuomPT1ts2bkzF0Vg99wnsSMs6mHzk639///tWn7ktQug0jZKQPaFz8pl+vqIyJXS+auXKzOJlfAomt29uNsKXXoiq/1MZ0fMxet9bT6LYN7ctfbIoxv5k+2rSRJRnHht57bsQZogsInhj0LILnfOXbGyvmCj2GXPZjz2G2PGRhIpj2wWXbMyxiA5JVk0HRA/UxuoAZN56CsD6444T3+ycFBq/PGeQRj2uWZtp5NFZRCbqLe8IRclEiNyvMsvZhuh4HPelrDxK/nCfbrzhBnz7wQed5UvxtqW3pUaP/wRF6Jtm1phj6XyLPlosTCoX0mHa4kczmD0HUzehdtNX84coni9Tx0lvfWvGDv3kJJWbdM3PX9y2LTnXAp/r/pgvLI7caeyedPIfqvhyBwrZjcT5hiO0JDHFr161KjNDJwawvOQRPeeLlWs2Tbb9Ij7YGtNYXZNdBiaaapZ8TH3jDZUPNQP2+u2zYV7DEgeWJvkBuPPne/5s9mPK1FUePj9c+mKOKFEWmCZj9EDtqzd9gb/qXe+CApK58USuRp6OJ590krXh4ToUGn8iFqWiOmyVcSKoiB2zYpp54EiqiH4XAjP1S4jNhgKluKIvmRB5zY6czLzQODeVLUeuhOghpPFrF9kackrr6uxEb08PttW33yPdtLE4IfqB/v7UAmuSb5woX/TcHnvMMZl589wfHnjPwty+cPPzz4sy//OrX4nlpNBA4YTUaV49xROS5yMP1ANYvWpVZm49oX3ikWbb8LpqjtWH0pRG9HzWje+tRxSDJGxk0y3xFNUTIh9js2yaDJsx9n31oSwfkFNvWS8MU19RNJ7XhlnG3V1d0Fqjvb0de82bl9hwodFY+3l0uNoJ7qNNVrr2tT8hR56HPLKmT9MC0dNb8lWvfGUqwx++7TYoAFdfdRUUGuNZJEPLeJoU0iDH8MeQAvCNf/qnqDewjcocQimDQpBFM+zbkJ2PN0TvVCQbypQCT+/r7c3McZf4TVs0351TqpFSCrNnz8bs2bMBNLYbNPXRkf7A5fdCujcKwH/+x38E3QspP/wvWxjx9PewzTZvhM2/XQnBE6InHj7eTkcaw7fNqDF5CcnzoznOz8fxY+qqF9Erpf4awEkAdmitD6nHLQRwP4DXAHgWwOla6y5V+xvjLgAnAhgAcL7W+ld1mU0APlBXe7vW+h6fc7Y/Y30ILuQNGqtLIleaTX8shSKPPDqK8oZSM3TG2Ctyn3yIKq9PRShWV8hz4LNh0wEjPhSNhsrEomFf3kKAQWgeQtsfG19Mvl35VyUi+r8FcDyLuxnAQ1rr1wJ4qH4NACcAeG09XArgS0DyYrgVwHIAywDcqpSSlxRktOm883D2WWdlKoh0tKWHUJ6HMEQmjy8hNvI0NCE8f/p//o+IvIpQsxt5FyqLpYl8IcWQieQkVOoKgwMDyTx2kjHHq312iZ/P8vH5J218MjQ4iNGRkWRrQ5rjToifz+ChdI1Gg6eYHb79oTmf3/QNyN5fVxlI8ibK5uPtiqXTSAMff5fKTtqIxNSxfNmyzNLFq1autHgu5CVkjF4p9RoADxiI/gkA67TW25RSLwfwfa3165VSX66f32vyUdBaX1aPT/HZyIforf4iHtFLskWQeKivzZaP1eHiLROVxlAs+oxND/UBkXqaVV4uxGpDmBDiQnwrgqi5LFAb129vb8fcuXOhWtI4MxQFu+yEHH35jEX0Mbp8+YyVVU0eo3+Z1nobANSPB9TjXwFgs8G3pR5ni8+QUupSpdTDSqmHdwJ45xln1OIpHcBFF17oRDDvuvJKKNQ2HuG8nDhKCK38/E0eSzY5ly5XPmx+SdeuIPFSHM16sNmW/PTp5LKSjv323dfps9CdzZ1/m48+/bwcFIB//sY3Cvsglauw/WYfAAAgAElEQVRNF6F0aTycz8QJ9Yf4KvX17M00Qt98jj63YdL8+fPR1taG3t5eAI0NSbgMzZgxbf7ff/5npx1zYxIg3UAqNP5glcLX7rlH1Eno/Kh16zLz421lSGPyfCOStWvWpDYhUUivTqnQ+BOWegH0jWD5smWJ/4Tyly7xtu8Jlf0xVnpm+UvdjM9Gav2XWuslWusl+wO47/77k24b0c9//vOUEjMAwM6dOwEA3/3ud7F58+YML3eYy/vI5I2Rs+ng8T4Z13AO96uMQNTV1eX0DciWp80fCEdbXnd1dAT5J+ly8dryEVLGIfapMStyT4DGw+Pzv729HQAwrz4DxqQ95s7NlR+ikZGRTBwtkBYzc6+npwdjY2MYqP+QRRtwcOIblWgAb3v7261+mz7yciOaa5QBl91///1T8UT//YMfJP5QPvmmINzPH//kJ9AA/ut730vpGh0dbfDVdf3kpz+FBvDTn/0MQGOzEtqghBZ701oncb94+GHE0pQeuvFNr0z8s6Qrh6xNl01nXmqGfKzOoj4U1ePLg68hK8v/IhTa2HKZInnKe59dz0Fe4vcr5nnkcUSxz6WvPEL9knSF6OY+++wWaY9CZVWTh27+DcCm+vkmAP9qxJ+narQCQE99aOdBAMcppRbUP8IeV49zkgLwjre/PZmmRL9FX3D++amuEi16Rkt8kuzKFSvw6le9KlUw1L2jJREuv+wysbsxFYnyyx+YiaIyG9uyG26zOx/KK4UyiTcG5jn/nd/X5XUNFVDo2LULCo2hGz5FUtJhI6lc6JyWLOb+UaChHHOZBb6wGP+gTB9Q6WewJ594IuMDbQZk84vOaWlhWhqF51UBeP8tt2ReOra8Ex8No1B7RIGGVEiW+MwhHpOUcORDOFwX0BgSMqdzhlLI9Mp7UUPk+wHYjtrsmX8B8HUArwbwPIDTtNad9emVn0dtls4AgAu0rn1PVUpdCOCWutqPaa3/xudc7BIIUuWDELe7InopP6E6QuVc/obmJTbPPv6iZVhEB3+x5vVDqps+FJzHR1vdznvvJDlbWcTY9+XfZjfkWXUd4eFx+cdly7Afo0PyXQUiemitp2xYDOj169frDRs2aA3oSy65RGtAb9y4Uet6mgb02rVrtQb0G97wBq0BPW4E37UvxPI3Wz6P/+vXr9dLly71ympAX3HFFaJNDehjjz22cH4kvaFpRWxLstpiR5Lz8ZVRDzSgH3/8cadNLYS+vr7U9bZt2zI8lUol2AczdHZ2ija5bxrQXV1dWgN6YGBAa0APDg5afeShu7tba0D/5je/yeSfwnHHHSfKUrwt/aSTTrLea87jKmcKa9asccYfddRRqfhly5ZpDejVq1fr1atXO3WvWrVKa0AvXrw4Fb969Wq9aNGilL6lS5dqAA+HtKVTegmEvIg+BsmUgRZduspG9Dx/oToQKOPKQ5llNdUptOcXqy8vX0zvNKbOxCBtwF0m5nCCZtdc1peHGPsxvXN4eEJky0T0RWVDEf2UXgIBAC6+6KJkDJ7Gwi679FIoAC//oz+CQmO8jTJ/2qmnZioZv7ZRXrlmkVT5zIfYRwq1qVohpCBvVuzyJdSHIuVY1j1QQphqJL1kFWpj1/STkem7eaQfmkw5eib48sCu/Js2aLy/0teXLHFAP0Lxhcr6ensxOjKSxA8ODKBj1y4M9PcnY/Q8D6ZfCsDpp52WSqdlm0879dTk3NyAhJdZ3vtrNJypYC5FbBujp6UR+ObhfLx91cqVme0G+bIG/Ccsc4ye4sxtCENpWiN61xuUKPZ6d6SYPEi8RctgKpRhrA8+lNhs+5I8kK/uxsiG6JB4qWFvbW3F0NAQ9p4/HwP9/RgbG0Nrayu01qmFz4rkwVaWvjxJ5WCTDbFfZnuUV1ZNF0S/bOlSAEi9IektSDNm6Gv8Te95DxTS25ARUcG89k//1IoGpOuiFIMqpgK5ehCxFIqqyiojlx6O2GLtxiLEUB0unaa//ZVKJs6G8DkC7K9UEjROwdwURKExk4aOXGdPd3dij47cf6UUButbAwKA1hotLS2gFS6lfJn2APt9MuugBERs1wrAzh07oABsqf9To4Twkx//2Fo/uD6+4Yi0abjZDtFsmdWrVmUWQDMRu3TkPWwFYMnixdF18SWD6IlsvDbypftosuXLsNdMH1woazL0mPpi6kmovtB4mx+2NCLX8FoMouc6JeBksxMr40PS2sKbpz2wlXXIfaA85LUb0h7Z/JLsA9NkmWIAuPSSS5IxenozUmYJwdNbkhfg8evX45ijj3Y+CM1uRMtseGL5JUTi4y9jS0UXIuI8GukHOS+ZevKSVE/KGraxkQudKgBjo6NQgLjMMPFsfv55VPr6knn0Et94tSrK8mtaKmFsdBT9lUoyr72rsxODAwOo9PUlPDwMDw0BSC9fANR6AHyZBMkPM9/bX3wRCunlHLjPUs/CdeSLn7nKg8evOeKIZF48LQVCCJ63O3SUNiChtooCn4PPz81xfnOjcF5mPpqWiJ5IeoPGku9N32z5MnTFyJXpbx4q454VtR+LBpth3+aPxN/V2QmlFFpaWrD3/Ple5Cydc+Q4OjKCkZERjI2NYf4++6CvtxfUVrS2tmLPvfYCAn0Nuaex+ZZkfGQrg1BZIK5uhJRDTJsmxU0bRH/hBRckf8QSsqdNwWmMnjL/1hNPTL3lQrpiPirayJdJE9HQlGnDhdgAuXwkZF52Odp8UAAWL1qUSvv//uRPSmnkQ/IgIUvJRx7f3t6O4eHhZA0WGzKVbBDKpbFyIhpXN4EgrcNCxMf9zT9xzfjhoSFrL4D7wfOnIZeHQnaDcymv3A86pz9zXcHUceTatZlFzfi1S16hMfvNjDPH7xXSm4GbR37PeV59NC0QPVHMAxmCGLjOIiggL5XVyEzkXXYhlxB/itzXEH1mnM1WaP0o4oMt3eUvnRNpy3XI+LZLxmXDJeND9KEyEvnqhU+3VB4+HTF5sPkVo8N1JFlTn5ouiH75smU45OCDUxk+68wzoQC8/nWvS73h3nnGGeIbXSLNjj5SSKO9EJLQ6USRhG4miijfJnLRLN0nT9Ssl5Tv/sfWj1B7RByl8dknHB3SmLVCbahmoL8/mU3C9RJypfF9rp90DA0Ogm9IAq1FnWY+6N4SoqflkYlXWiaZxtPpOwId6RsAt2N7ds34PPfG9hy4bCnU5sjTDBlaflhC3QqN2TY8HLF6ddIL4DN06Ehly2fnLFu6NNkilcLqVauC8z3lG/q9994bBxxQW+5eGXFAYylTih+qfwyigjj1He/ApZdckql0RPxGSGRWJtOPkMDtuGQ5H13zl5fPf1debLx5gs0H1wNq88flN39h+O6XpNtmSwoEHiS/YssmJJ903tPT48wPHzahaYsS0RCOubSwOe0RQLLPK4+npZVtZPpPPtFyxUR71cfvTaJnk6ZZku98OWJX/ZL4pDpNdeaUk0/21hOSoWFfvpsdkbnEMJUvLbE8a9YsALWpkkDt3kiktU5kqDwkX4BGmdIuUm1tbakpqqQvlHaroRsi82b6unJc1sXr47PxFCGfztB0F18ev0PswmPTlV6mL2XqaMY9DtEbWt5A7W/SlpYWVCoVLNx3XwBhQxUm30B/P8bHx9HW1oY5e+yR0WGzn+c5lHhD6q3kQ56hGvOIQF7JdqhsHvu2vPpk1XQZujl/0yZcd+21qTf2+Zs2Od/U9IZ3NUK0jAJRyPjgZ+68MwghcDlXGu1CFCrL46QhBhuyDCWFxlQuG4UMS5kVtIg/oQ207dqHyE2+iYI9NjRq83Xnjh0Yq6PKsbExKKUSRE1DJWDyGnIegQZiJHTKlyLgoburK/UxUwGZYR+X/9u2bhV9lGRt5USTMCiYU6cVgBOOPx4KjR8oTz7ppMw95c+Lb4jOWn7GkT7G0vLI5BddE9+6I4/MxPHplhQ439IlS1I/SkW3Qy8VRO/jtZHJl6chsNm1HfPoNfMZ4kMe/ZNZS0Ls+17sCNBRxH6sDkmnr/7yc/PapcvXKITUP6kMQ59DW1wImTa4r6HPlisPLnu+tiTP0aeD++HzfdpMr7z4oovwmj/+40zllogXzMYNG/Du66+HQno/SQXgPTfeGPVWVAA+9clPRr9JTXmF2kYqCsBHPvzhQg0IRynmQ53XR0m/gn23+6lErkb+D888k5zTkfvvyk8ZLzleN/kHS5t9Wqhs544dqY+epi7zyNMUaj9LDQ4MYHRkBIP1LfzoKC2EJoW++tg991fKC19eIUS/LZ3Q+TFHH518BCXETD8uUSDET7po8TNX+UrpCsA1V1+dSSP7tAQC8fK9YW1LIaxauTL5oGv2mE0eCvSjFIXVq1Zl9ryNod0S0YeS9AY101xjY9J1nobZ95aP1YUAf0LzGmN3KiP6oukmT7PyWrT+8XvPeV36ujo7MXv2bMyePRsjIyPJ/rGhSN7H58ubz3cXSfm3+RR77fJDel5t9mOOsTpMfyRZNV0Q/bnnnJNb1nwI3nvTTak3qPSrP+ez6SviCz9KPrh0SA82RyO0NEQo+dDORDbyNrTiQjFlDKu86ZBDnGUWi6BcsgqNaYU8mEsFmPwj9U2iTV5Th2TTjKfZLebG1orxSiEE8fOlFzhvZ0dH0huhQD9X2QLpOPUd74BCrXdO2y8SkpfyKcVxnWb8h2+7TdRBDSn1Ho5cuzbzTYAjd/KLbyVoTsOkOL51IOmSljmga/4z1YrlyxFKuwWi542b7+0Hgx8WHpfMRKJhKd31No+x6SufUB+LUqx+nt8i/oWWXTPLKPQex6DVGJ+kRszmo88vm34XgpXs+vJn0xXiY0x7ECIzmYjeJ6umC6LnpNAYE6PNf2k8jfPdeMMN+NAHP2htOCXiN9tsbKQ3f16yoQybT5L/NjmqBDQnmHopLn6fr3QsM/+x/HlsU9mFlLHNbohPSggUr4U0PlPmxW3boJDd0JvS+ZLEIY088Q3096PS1wdAXhxNoYHcbUsRSOPunIeWJKC8dXV2YtfOnRjo70/98CXp42XI8wEAP/7Rj6AAPPDNb1rviwLwm1//OpVunrteAJJdcwYNbSzCx+jpyOMJpRPCX71qlbjBt3nkC6GZi5qR/Wm78YgNyYS8jeHh8VFeubLki+jylc9E6ZHkYnRJL+oYtGvWg7xUpAwl+7Y8uOpyrH1TJxCm33W06bDxdnd11fYrVQrDw8N42R/9kVeH6zrGvi8PNllXuXD7eY55ddjyqaYDopceXjOeH5tNNhRRVGeo3tBeCemcuq/w/C8MG/qy8dvsxZS7JG+zx5Ekf1ClPCg0kHTIcr4h916hsUTB0OBgMgfehsz5PHpC+HTs6+1Nehsc9ZvLEpu+VatVtLa2QimV/D3Ktx+kQIuMUbzr+wPPLx3pWwHXYaZvfeGFlF3Ky//+5jfOcpfsEWInpE3xhNYJndOc+DVHHJFZPoFk6HrpkiVQSKN27guN4y9ftsxTOobfuxOiD228pLdgCK8UH6KLx7sQxGRQ0Ua/zJdGHl1572EZJNWDWFkfD1EI0g3Va/JJSDLUjxAdITpNWZcdyXcborXxumy48uerYz7ZkGMRHZKsmg6InlNo5Y6ddVK2/c/dfTcUgHdffz2uvOKKpvgi2Z0IO0VIobHZc5m+csRcJvGGgygEabqW5iWeXTt3pjYNMeVNxAlB1nYtyRKPNNZP8f2VSgbZm5uDU6+A27PNoDGJ0HVvTw/6K5VEhuKlhdC4LY7QKZAs5bevtxedHR2ZsjPLYfPzz1vtcP8V0ouaEWLnSJ7Pp+dHc1EziuMbiPOtA22kUEP/obRbIfpQklBACK8U75N36TTfwkWoqJ6Y8vDJT5R9CcHksTeZ5Ms3NfSzZs3CyMgI9tt/fwDxiFqyZ177ZKmhp/H0uXvuKaJom46QZ4yot754G9+8JOSehTyrZKNarUIphX0WLBDvQZ46xX11lXcsog/RIcmqshC9UuqvlVI7lFKPGHG3KaVeUEr9uh5ONNLep5R6Win1hFJqvRF/fD3uaaXUzT67RLabYUMOLhlJxw3vfrf4Ri/SUGjjWEYjn1ePrXxs8RNBofkoUnb8wXHlNbQcfHy8DnE/bD7Nnj0bc+bMSVaT5GPYtNRwT3e3FdFSsG0WPtDfn4zX881CEj5jHJ37qFBb+pjQPkfwfA0cHjo7OjDQ358qLwKYNAvIthGIrXylfHbs2gWgtiLn6Ogo+uozjYjPnPlj02WzvXbNmmRWDf8Dl+vkSxETSl+5YkVqBo5C9o9YPnZP4/F8Y/FY8iJ6pdRaABUAX9NaH1KPuw1ARWt9B+N9I4B7ASwDcCCA7wJ4XT35SQDHAtgC4BcAztRa/85le4lS+hcxmUH8eGbRdB9PHuRgog5OsWOzoT0Lm58hsqH+uOyEypdNobqL5sGllyhkfNlM475JvD5Zlx/cBtfBZbg/XNaXv5BeQSgKttmw+ZrHfogfoYg+r6wqC9FrrX8AoNPHV6cNAO7TWg9rrf8A4GnUGv1lAJ7WWj+jtR4BcF+dN4pcb3OFBjq3pYdc8zf5Bz/wgUwcD5/9zGcyumx+S/HmuWbnIWXAAx8rvPVDH0r+BLYFmhnAw1lnnpmsGRJSdlI4853v9JaL6x7Q/xIxdiVfJdv8XLrmpBHni4uP+1Tp68O2rVtTW/u5/FdojE2b4959vb1J72C8WsXQ4CBGR0as6Lu/UsGO7duTdAm1j42OpmbfdHV2JmPuJq/0pyx9r+jp7k7lgR9t4dprrsEZp58OhcaqlK57BMMPmhX0wpYteGHLlpRe6qU89eSTTvvHHXts8ucrjc3Tc0aBb0xCeTIRPSF4SqNrGqs3/3o1da5csSLhIX20NWEIFfkYe5VS6n/rQzsL6nGvALDZ4NlSj7PFZ0gpdalS6mGl1MM7kb6ZWghm2qfvvFNMl6452XR+9PbbvfzXXX+9c7hG0s39cA1VuPIj6fiv730vlVapVHDQQQeJssRDQwc8/oknnsCCBXR7/b5I4d777nP667uv3/7Od7xl6rrvEvlsutAd5wnJgwrUOzY2luwBa8pLfhPRkMvY2BiA2sYX5kYVIyMjqFarGB8fz2wSQlStVjN7xJpEsm1tbY29aZWCUiq1KQfQ2JCD6zePUn6k55Pi77r7btz/9a9DA/jmAw947y0AbNu2DQDQ2VnDqZVKJZO//vqQkq1cEp1aJxuKUDmb98jMG98khu5Pe3t7pmzomnSTH/Q8EtHS1ADw/37+85S9EAr6GKuUeg2AB4yhm5cB2IV6Wwjg5VrrC5VSXwDwU63139f5vgrgP1B7oazXWl9cjz8XwDKt9dUuu0WXQICQ5uINkY8hqjp55H15M+N9Pob44bITKhvia6z9EN6i9yhEVirvGFl4eImnu6sL8+bNQ0dHBw542cuc91h6DoDspiLdXV3Yo765iFIKs2bPzpRfd1cX2traxI1IgMZmJ9VqFaOjo9h7/nx0dXZizpw5GBoawoKFC6N8lfIvpfO8uXh8OqX7ENqWSPZdvtqONh15ZVUzp1dqrbdrrata63EAf4Xa0AxQQ+qvMlhfCWCrIz6YFGpLFisAV73rXVAALjj/fJ7pTHeO3wjfcgCcijQgB7/xjckyqy4+JVznaZRd5NN39VVXiX76UKhCYxu2c885J7hcuQ7qjvv4zO6t6aNLxqbHZyuUpLpnxg0NDlqnNVIYYMjS3CbTV3/oSDNalFKYM2dOgjxbW1sxPj6OsbGxjN6kAanPuCEif2k5Y0Lx9BMUxZlI09TH88d/ZuKBhlR4OtWt49evT7YGPOmtb03xuJC9qfNr99yD+++7LyX77w884KwjFI5at8669AEFGmahj7B8+MXUbS5drJCeVqnQ+HHKXDKB7BNv6dMrBUT/cq31tvr59QCWa63fqZQ6GMA/ovEx9iEAr637/iSAowG8gNrH2LO01o+67PJlikPffonfDtlMHi3xRci0n0e2rF5LSN6K5L+Mso2xH+trSNnFvFxDZUxZIOwe8RdYzP136YAQJz0rNrs2HeY198floy8PUrzNd5uuEB2hdTbkWQ5pn2z2XT5PCKJXSt0L4KcAXq+U2qKUugjAJ5VSv1VK/S+AowBcDwD1hvvrAH4H4FsA3lVH/mMArgLwIIDHAHzd18gTXXD++bjwggtSBcELhDYAJz4KJg9tQGI+rCa5HqK85EPDNpLyKj1sYDwka9vg2EXSgxmqg3x715VXiuUfoyNELm9vxuUbrxcuXzhSlNCdrR7SkX8UpY+CQ4ODGBkeTqZBSouccVvEQx88ifg10JheyX9Q6ursTP0wxX+cGq9WxQ+pUt6kqZKE6G09G5LlvXOaJHD8+vUJuuf5d91XhUYPQPKVNgLiOs3JCUT846vLtrIcOQ8tW2zal3SsWL5cXAgtlHaLH6aAcse1QpESAtJDefJSHt1cppn+mfptdmLKsNm++nyAx77LzxjfpXsE1BrgPffcEwDQVv+Y6rMXUrd9ab09PWhpaUF7eztmz5mT2OX+5UXlobIunVxH6DMdUh4heQnVn6c9CtEFZH2dNlsJnr9pU7IZuIRUFRpj9XRNm4mbhSL1BqTrouRCFrZrV5p5HfMWj33j59Wl0JhutnHDhlT+Q30wZWkKXZnEy9p2f6gH5rp/tnpki+N2eflQoGWE58yZg5GREQwNDQFo/EBFP06Z+mw/VxFqNm3wDUTMNKA266O1tTU1K4X7S3G2pR3MrQ7N+KHBQWuaVB4mmfeDflSiumJ+/1JobA7ONxG/7NJLoQB84fOfT+IefeQR68vTRQqN5YgpUP0ndM43CCEkvuaII1Lj9VJ58M1KJPvSffHRtEb0sMTFUF65vGR7w7t4YnXGyvjkY+5HqI4yKca+xG828jHlEuODmX+imN4pl7FdU5xkP1ZHSF01dftQto9in/+QdsGn22U/j1+I4PXJAtMI0Z9z9tkJkqcxeHpD0zVQyzzNxqFrGOfXX3ed+Bb0vRWpYGPenjH6OW9IpW/Gi4eX12mnnhqdZyor2p7N5qek14WUQ1BWCJ+LpN6feW36R/XBbDRsKMuM43xcRqE2zdGUNY/JudZQaIy3Dw8NJYGue3t6EuQ9NDiIvt5eDA4MZJYYpsDH3+nHKNIx0N+P7q4u9PX2Jnb4Jia2PALZXgDp4BuSSERp5vNtkvRSVkiPzUtyZtrzzz0HBeC/v/99KAD/9dBD1nvEx+ol/SYfXZuInuoRzb7hSx9IftIPU7QsQlTb8lJH9L7GNbTx9cmH6Clqy2UbHt3ctlSGLv0uNFRWGTYrnfNJ/DF1rYjN2HIHag3u3Llz0drairb2dgwPDaFaraKtrQ2zZs/G4MAAWltbk+mW5qJlNp18kbO+3l5Uq1XMqY/fm3PtQ8hWJ2LqRugz7KqHcKTFtiW++h5iP0anJKumC6I3x+iBWuYI0dP15Zddlsq8hN5tlclWcWLelpI8158Xqcf6YZYL/bJ98kknJXN6Q22HvJRo3jyVO41dmrMjQn0uUt4+X80GrIgezqPYufRS5aiQBz0+DoX0DBmOFHmg+L7eXgBIfo4y/8xsbW1N/bE6OjoKrXUyBs910iyc4fq3AaUU2tvbk/nzIyMjqUXPzHyHBL4ZOiDnUbp2kUJ2yeH77r1X1PW2jRuT8X0zzdR1xeWXZ/yjc0LofKaMufSwzUeF9FLHHMHzpY+Jj8LiRYsys26m3ebgQHljcbE00fI2dGDT5dJva3xiyWbD5qsrD2XaL0M2RDd/cfN7Ykvj8rYXQQzCtKVxH2zXpm3JxxgdMWVr8zVElseFlpWtnPK0JUVl8+ow88jLrP4SmB6I/pyzz8am886DQmN2DSF6OlLB0DxumnXTLGqmbt6A5GngCAWYss14ndt6ARzNusiWXlYZ+9C8r9Ezy9E8SmUsoUgFpBYTIxROfHy+umJ6aCaNuXAYzbaxvTiGBgeTBciIn8bgSR8PgwMD6O7qSvkDpGfwEJFejtJ5ML8HkG/dXV3o7urKzOPngZOpg2+sTsdn//CH1PW2rVtT1089+ST+8Mwz3vqg0JjBQ3/jrj/uuGRuPaFuuiakz2fUEGo3/6ilNKkXIOWJl4erjFw0rRG96w1KxK85SekhMtxuXsoj78pj2fryXMdSUX02/phyCfHBZQcoVv9C6zJQa+jb2towODiIeXvvnaS7ehzDQ0MYGxtDS0sL9pg7V7Tlsy/5zIFLV2dnskDawn33tebdVS62MrQdTT5bHkJ0+mSLtEchzxCEODVdEP3FF12UGZOnNW+ogGiMno+vXXnFFQnKpzi+lPG111wT/IYkvj//xCe8qEC6SbFkeyiL6LMtGRyjg89fPv2001LX5hZrXDamnG28IWVixtkajNAeD0daPh6b7zYe5QiUXunrS/0lq5DdSNwcXzfXoAHTI9kYHx/HnDlzkjF80k38fb29zrn4ZpA2AKd1eGgVTdu3AgrmZuESmbwP/+IXKXtPP/UUFBrz+ilsf/HFhOfFbdsyDbDJ+41/+qdMum2tG76lII3h860HV61cmcTx5Yq5fb4hyRGrVyff2SisXrXKUUJpmvIN/fDwcGY5UJo9YFZSAJmPRdKyq5yHZhFIpADcduutmQpH/vgeaKDR6NsebH5tBi7H9dl85jpv/+hHk/gX65XdZZeHc84+O+XH/vUt7+j64IMPTvmwcOHClP6zzjwz5a8t32aayUvdaFdjyPXZ8mizIcX5eCV7Pr15SVoO2LZM7cjICGbPnp3UdVqbnvTYqKWlJUkfGRkB0FhyVyllXcJY8pUTPaNkh5bl9elw3W8i7hctS9xtTBkF0ssnJ6t5WuzPmzcvk27LP1+WmC89TEsOK6USXkqzrSnPbUn3uvRliieLfEM3RK74UFkbmek2HS7iqKCM4ZxYkvKQV9689h3LsG+WW14KtclfqDY5nh8IfBIPLLw2X0K67rb7YtoKqXu2vOepv1K6zbcQP8DibOmype4AACAASURBVPZi6mVoW+KSidXhao9CjpKsmi5DN+ede24yvZI+xtLQDVDLLA3d8P1fr7/uumSq5XtuvBEKjQ+1IQ2IAvCB978/4f/Exz8uIgAXctPGUbO4ELIhzbxU9AWjAGw45RQoNJA6r+TmcJhiaXSvfDbKQsKhZOaDH23+UJy0JIAp19PdjdE6QobAx3VyeQWkdnaiNL6zE8UPDQ5icGAg+VjaX6kkH0a5Xle+zOPw0FAyJEM7NtmCZINkpLyaob9SQV9vb/LBtae7G709Pakds3jYsX07FBo/cJHuLZs3QwF4/LHHMnmicxrmoUBLI1D6l//iLzL5omnLtPMZDdVQ4EM55o9TvFz5rlESUTwNAR126KHRz8e0RPS+t2AMKpF0+OxKemwyPjL5YmVtPsRSTHmXjejzykh+xsgB4ffNxc/TfWXhKmuuwyZf6evD+Pg42tvbkw+rkn2XPU6u58BGsfddobYmz8jICAYHB7H/AQegv1JBS0tLMq6/17x5wQjb1R5wH0PaFp7/2PofYz+0LVPTBdGfc/bZyY85dKSPrvwtSR9ezWmXlEbLFN/yvvc5Kx1/e3/0Ix9Jxun5DfrUJz8JBeDTd9wBBeDzn/tc5k3L7ZhokedBIs3OizbaZRD5QYtH0RS0kOWRQx72WLTio9DyItuuuuHSb/ou3V8l8NniKBAST8bY0ZjWyHmTOKWwxx57pMZwSYYvE8yv6QcuHvorFXGxNFcwETh9/HQFoPZjF72kknKrb1nIP+D+v5/9DArA1hdeSPlFCN8kXoa8vJ/5/e/FvJkTQcBk6OMsIXZC8oS8uQ36eGqm2T7K0pE2IFm5YkWy9AH1ApYsXoxQeskiei7r0gXID7/Nrou4PklG8rsImRU1D5q2yYXm36WjiP0yyOWzD4H78u+SjfHPJFc9h+Va8lXy0eWndB8ku0Xz78qLZD+0PSiqw6a3LETv8v0lgegvuvDCzNi8tMGIdHz39dcnKJ8WRLrpPe9J8dz83vemdH3wAx9IHWHwcnuf/PM/T12HVGQNP9ojnrIpRif5d8rJJ2e2buP6+JHrOfusszIzb0J9OPaYY5JZN2WQ66HnPOaRyykAtLjYeLWa4ZVkVY4w0N+P/koFlb6+DJInxFzp60NvT09qmWIwm3TOp2S6fDYDbSloxu3Yvh39lUryrYCPoZtlGJp/yiflddfOnYk8X5ANsOfBtDnQ34/enh50dXY6l2Lg5QWkG3kam+dj8HSUNhFRaKD2FcuXpzY0CfGdZI9YvTr5AUt6eflot0b0tsaVF0azEb2NXP6ZfnEfY2yEUB4dIfmfKB15ZfPoMMvK5r/0sEnINabcpfqpUBuzHhsbg9Ya1WoVe8+fn0oHao336OgoWlpasOdee6X8MflingPJL7Dz3p4etLW1YWxsDHvPn5+xy/UQ2fxQAHbu2IHZs2dDa435++yT0ml7/l3tgUJt2Gl0dBStra3OMvKVR6h91zHUrkvWlJk2yxSfv2lT8pPPeeeeC4UayudvQxjXtNH1dddem8y6oTRz1o30Rn3fzTdDoYHoP/TBD2Z46NpE9BJpyDZMVO9qPEJJQgJ59Jjy1BM65eSTMz9ISQhI8oHQC5C/oc77crDl3xfPe1tSXm16pDJSgcEmX61Wk0YqmdNeDzTmrLXG+Ph4akxeMT5agoF+euJj87blDAilDw4MJLOLaCnllpaWZLE0IIu4XXWDZsiYSx0Dtfn2tryQfvoR6ic//jEUGuP//Acp0jk2NoaRkRGMjY0lG7rQ1obSfbfdO/7DFI3NE+rmm4LTtbQEAtdJgW8STulHrl2bjNETso95xndrRG8j3miWhehDZSVf6BwCj0021IaPisi48h8iGyPn01eWfIx/rvqQxz9bfbDVDf4w2/Jhpkv55Wmh9cr3LEl2XbpsvC6dEOJCEL2k0+drUd8nEtHXG/vpgegvuvDC5M9MoJY5GrO3BSo484GisXi6piPNxqHw/ltugULjj9iP3X47PvLhD6fiKNx9110pXYTwbW9aQvIm2XhdOsoknx2zDM1Fm1z63nnGGYUbZ8kXm70oZINs3sw8mvpsR87L0aCvbnJ/bXrN+JHhYesYOM0xJ5TOkS23Yx65Lh74ImdSWfB4/h1A8oMQNSF68/sDjDx07NqFgf5+9PX2JnF8iQWuk64J6RMNDgxkZg6ZyF4BeOS3vxV9P+3UU1M91JA6wuPpL1hXuft0KtRm2ygAhx92GEJpt0L0ROYDE/Im96GM0LdwCK8N0Zt6zDQeZ/PPFW/T4/LLxuPLQ2hPKm8+fTbyUEw5uGQh8PD74EODecllv9LXl6w17xsrz2M3ticTcv9Dn7venp5k+QSlVGbc3uZrLKL21ZE8ddjWLvFzlw7JvsmjpguiNzce4W9L1OPMefUKjbmvl192WWrJYulNTX++0vz6D33wg1BAguL/7GMfS84/dvvtUGjMn+d+fObOO0UfiXjDbIvzkcQrNQA0z91FVGGojAmN23hdPr3pkEN4JYQC8NWvfCWZsSDdR4XGtm80R5inl0ESmnalpRCV1sn4tpQPVx2jWTO2vLtkJTs8nq+p4ls+OCaQzf5KJWOfxv1tMnTs2LXLO2OGjoTwaVnlarUKpRRmzZqVzK2XFk0zr/kyxiY/lyHenTt2QKGxpSDnO+P001NLC0t2acyebyoi3WO+HSHnsW0tmPdZ2K0QvfIcicx4m6zE22xE73vLu+Qojvvi0uPyK8Sez0ebDikeHh2ucgq1H+uXlB6bh9CyJVkXf2jZ2+ootyHVYZ+PNj7ewMTWZZuMxGvLW6wOm06bnbLbAcmPvDpssmq6IPqzzzors7YNZdJcN8V8K5pLE1MaIXs+Jk/rsoDx3/qhD0GhtvIjxXH7fB4990MiqdEGi6N425tc0i1V/JjGUbOjjULyFpsWYz8PxTQK5v0x7ynNTVfI3mPp3Awa6fy56giXlWayUJw5fx3GNa0rQ2Plw0ND4no8ZqCNw218gwMD6Ni1K5mzL+VXGpsncv3Ny3lt98ScAeTKC9jRXPKYeGi8n+II0dO2hP/6L/8CBeDBb30r4aMxepovTz1mmhnD/4zlz/baNWtwzNFHp3TQjB0+B5+O5vaBFGf+LRtKXkSvlHoVgK8B+CMA4wD+Umt9l1JqIYD7AbwGwLMATtdad6na+qJ3ATgRwACA87XWv6rr2gTgA3XVt2ut73HZjp1HL8XbZGKPqTLJKQMh3ka2vBDFIOxY26E+ufIryRW1W0QeFh1SXQmVN+OL+mijIvUv9P5T4zk2NobW1tbM5uGUPjQ0lGwyLm0aEmI/tpxcz3KsDsmPPGVYpC0p274qEdGPAbhBa/0GACsAvEsp9UYANwN4SGv9WgAP1a8B4AQAr62HSwF8CQDqL4ZbASwHsAzArUqpBT7jm847D6hnipD9pZdcAoXGfHoKhPCvufrqzNudkDxd02qWNDYPxv/h226DQgPZm+GjH/kIFBpj9jQ2T0da84Y3zL7G0Dy38Uo9ACXwKSBBD1wuDyk0NhihscqTTzop47cZphJJL0uKD/WX52/E2JdASveVB4+zydji+JEQL407E8IeGR7OrHQp+dXS0pLMX+fpo6OjaGtrS60lb/OLr7JZ6euzjqv78mxumm7Lt08P9SZ6e3oS5E7bDNLWgnyDcX689UMfSlar5PPn6UjfmUiGnhP67vSWo46yzsXn4/+E5E2kT3po/D5mrRtvQ6+13kaIXGvdB+AxAK8AsAHAPXW2ewBsrJ9vAPA1XaOfAdhHKfVyAOsBfEdr3am17gLwHQDHB3sKJD870MYjvDdCP5TwDRqAxmYAN95wQ0qWNlh47003pfgpXtoggXSZmzIAjY0AzI0IbA0hJ+mtzSucq5GXKjmV03tvusn7cIU8fFQme9e3p9u1a5eQk2x+bf7xspDKRgn5DtFhBs3OJT0u+bLIVQZl2qH6R7NV2travJuGuDYkofSWlhbMmjUrqVc2IrvmB9XQTTLMYSHTL3qBpHzy6KJ0ag8GBwczvtPGQ3zzEE577bVXspEL+UTXdOyp76JFRO0EfSQfHR3N2Kc2zdyYBWi0T5RuxpH9vY1tIn0U9TFWKfUaAD8AcAiA57XW+xhpXVrrBUqpBwB8Qmv9o3r8QwDeC2AdgDla69vr8R8EMKi1vsNmr5l7xtqOSX4EHVKaT5crjuuTSEoL9dVWDrb82Ci2zELyMNHkuycSX4hOIi1c59XLfTRJshN6HdqjtN1b7odLV+jQka8OuZ6bGPux7YGkK1S2zPbIVW/rAKHcj7FKqb0AfAPAdVrrXherECfVWYrndi5VSj2slHp4J2rLHtAiZpdcfDEUGkM2dM33haUhnGuvuSYZsiFUS5uT3HjDDSkZWvLAha4o7Y5PfQoKjb1jaas+Wq7Y/JFKqpiuRsGFUnhF4b6aw0MK8q/XNn9cpNlR0qVQm4LmK8NYKkOPr7FQ7CjJSyGjQ2sMDQ4mi4vl8ZOCuTxxb09P6iMoLdJFH11p+iYN2Qz092NwYCA1hOLKB9kZ6O9Phnv4Egn0wTdEF6WTf91dXdblEapjY6IsBfMDrzS9VboPZIv/jNWxa1eyGQkd+fLEfPOS++6911pn6OMr/UhIwzB0TcMxxLdi+fJEloZ7iJeWTaB4GqYx945985ve5K2rNgpC9EqpdgAPAHhQa31nPe4JAOu01tvqQzPf11q/Xin15fr5vSYfBa31ZfX4FJ9EUw3Rx9p3kYtHaphs/NILw1UOPtsh5CqrqYboeVkSxd6fkOvRkRG0t7ejUqmkNsjIQ+Rrpa8PSimMj49jbGwMCxYuxPDQEEZHRzEyMoKF++6Lgf7+ZL2beXvv3ZjzrhTm7rlnotNVf3q6u9He3o6WlhbMqe+nGtPrk3hs9c8l40r33QPJbmh74MuDT28Z6DxPW6bKQvT1WTRfBfAYNfJ1+jcAm+rnmwD8qxF/nqrRCgA9WuttAB4EcJxSakH9I+xx9Tgnnb9pU7IEAi19AKTf/oTgr7ziCig0liSGwcN/nOJH+nGKrv/sYx/L6OBH+hjL4/nSCBKZqBzsnCN+iYfOTRQPIw6QH4JQMm3xePNHLIXsRi9lUwx64X5rdk6BlyMPUq/Jd03fc/gG9JJfof7Thhta62TMlxYSI3vVahVa62Tsdnx8PPVhdWR4GKMjI2I+6aVg6iMZk8+2rIEZ+NIL9OGz0tcnLjjmuwc8nU+rpA+qNhk6ku0nn3giQfKUZi6qppBdEuGfv/GN5JzaFfpAai4JwuucQgPJSz1rSuMbj/BpluYHXfoIS9MrS914RCl1BIAfAvgtatMrAeAWAP8PwNcBvBrA8wBO01p31l8Mn0ftQ+sAgAu0rgFzpdSFdVkA+JjW+m9ctqcqopd4fPZ95LPP9Zj8Njucxzd8EdLD4L6F9GB86T4qKi/pA/L7LpU3b8RjylfSbfrH9bp4XNch9dzFJ/kR+jzYZHzkKleb/RAdvueeX7vaEol3qiH6BC1MxbAY0FoIl1xyidaAvvjii1PxV199tdaAvuKKKzIy1113ndaAfve73601oG+88UatAT1eDyavef2Rj3wkOb/11ltFHulaA/quu+7Sn/vc58Q8jLPgipfOuQyX14BesWKF06bNji+N9B1xxBFen326Q0KI76E2eJn5yn5oaCgjMzw8LN5TW/DdRzN+dHRU9/f3676+vpSOSqWiK5WK0w6l9/b2JoHzDAwMpK7N/GlA9/T0aA3o/v7+VHxfX5/u7+/XAwMDiR3uT0dHh9aA7urq0hrQu3btSo7kiy0P0nNI/vb39+vh4WE9ODjozH9nZ6cY/9vf/lZv3rxZb968WXd3d6d85WH79u1i/Je+9KXU868BvW7dOpH3yCOPFONXr16dnK9atSqVRs/SypUrxWdXA3r58uWp66VLl2oAD4e0pdNqCYQiiJ4o5C3MeV2yPvuhulx8YHGSDhtPiA6eFlp2ks95EZ2rDEJ1cD2cx1eGZh5ifQrxWaE+zDI6moyvSzZ9qHNwYADVahWtra3Bm4P76n1fby9mzZqVTLGdV5/aV/Q5lMrAtDs0OFg7VwrVajX55hBS7yjt0UcewQEHHIChoSG0tLTgFa98pVeH6znLW/+LIHqbTCiin/JLIBApNLYQpGtz415eqRRqY2q04BnXZW5IYsabY/VmvELjJypb+p2f/jQU0j9OfemLX4QC8MUvfCHlG13bGllboyDxhfD6GhnbQ7d0yRK89cQTU77T5gi8MtPYoTnOaKYrNGYX8HizLMzrQ9/8Zhz0+tc7G2t+T7g+Vx6Jny/QBctRsmPLiytfNuLAi/iHBgczSxtQoPF02u4vka2P2ff19qZmAkl+0Qwdmm1jzpIBat8B2trako26geyPUSSz/cUXodD4cau/Usksm0DLEvDAF39raWmB1hqjo6MYrv+gxhdtow1IqHx4/hYsWIBZs2alNkyn2TYUaGyeNhrn6WZjS0sh8E1DaDydL1hGY/nmpuF0TuPudOSzbfgmI/wYSrsloifi15zMRlQVPCKnrM93X7xk35U/XzlwO5xPiguRlXyX+Ivcw1jy6c5rw1fWPqQa4gv3J6YucR0h+XPVO+4X1xtyzX0PqSsuu6GIPq8O1/NR5MjtF5VV0xHR8w1HwI78/Nprrsmgfj7nnjYLp41J+OwbWgLB9IOWMubLFlPhE7L/wuc/n6B7QvYU/vZv/gYKwD/+wz+kfCeSKpsLzftQLq/U0hLG0kPCewwKtYXmJERhe8jM37u5LhOxcJ0KwGv/9E9T84eLEr+XPC0k2HhdeqSFuCReIs5Pm28oZBE9oXBzs/C+3t6Er6+3NzMvngfbZh7SfaE0Wn6Y0HDHrl3o6e5OkHxXZ2cy/58QPeWB6rdtMxWXXYlHodGTsJXp5ueft+ogWZrJ89yzz2b0fPkv/gIKwPHr16fqNV++gK6pbtP18mXLkt4wxXFkzxdGo2PTZ91MJk3GrJuydcTKhpCp17wGizf5Q+JCySfrKoc8umyyIWSWTd7y5n7YyhNCvJletFfE7dvKua+3F0opcYze50+ofaDWsM+ePRvDw8PYd7/90LFrV7K37by990ZXZydmz56d/N6/17x5wX5IFPsMudLzPI9F2pAZRO8hc5liyhzfHJzSzfF4hdoYPiF42jCckDuN0ROiJxn6Q5auYZzTH7B8/vzH/+zPoAB84uMfT8V/7u67kwXOKO4vvvSl1PVXv/IVKAB/97WvZRpsG5koWwlxoTpCyfcikXQrNJDPxg0bkvMifuQhs8fje4ilQGmDAwPJXHRbI+/Sw1G4zTb9KcoRfV9vbwYVE3KmeBqjp3VbTBDH57Fz/eaSxma+KZgbjNOSza2trdBaJxuC0Hg62aWPt0qpZI0X3rOQ7NjKcMf27VDIbhn4q1/+EgrZcX/iJ7RO1wrA0089BQXghS1bUrqkJY1t94zG0WnM3lxS2Ew3Fyijb1QkwzcYofpJ8aRjxfLlyTkhetqaMIR2a0RPxOPNo002VpfPjg8Fcf0ue1zGRjZdPr4QXXns2ORCysFli8uG2vXpjUXYkh883sVn009UJho0dUr2Y+o999P0I/Sa+24jl595nr+JeJZjjrwcisqq6YLozzn77GSbO1rbhsbdKfBxd3Ncno/R05HWvjEKLBVvLl9MMrSlIC1TTEdC9NzGp++4I/lLlgIfqydeGquPJemhzqPjvHPPzdg3fTTjzG0BXURle9S6dcnYI+nYuGFDphxIhusIeUmZ5+ZfzfwB4fzcV0qnwJfJNdO5LPfDDLZt9yp9fRgaHEwtKazQQO3SMr18tgs/9vb0pGbpmPp4vePXdOTIu6+3F/2VCgYHBpKeRGdHBzo7OlII2/ST/ojdsX17ZqMPW+Dp5h+9HNHTn7dPPP546prrIn7zT1q+LDGVFV3TH7QUHvjmN/HXX/1qKo5ml9HsG35Ns20InR+5dq24RaDJa/4p6yLSG0oziD7gWEQHyXJfOdlkubzJ70rz2Qzh8fG6ZDgfIJdRCIXaCbmOte8q51AfbTqo0W5tba0tAVxfztZXTr789fb0JMsK77nXXglfSL2z6aSNSWg9+j3mzsWL27ahvb0dSiks3HfflA1q6EdGRtDa2gqllHPjct9zIflqO7ryiEAdUnlw38tsD/K2Q2q6IPoLzj8/QZt0NDcLV8iuYklj+JdfdlmyVSCtg0NHQu60iiWNzdMY/vtvuSWJv+3WW6GQnW1DSJ62FKQxfHPz8Ls++9mUr+b8eTN87Z57rG9yiZ+QboiMOX+XAv2TQIHWE5IC1wl2tAVCLxdecEGyKQNtXsJ7Uq5gI1s61/1fDz2UKrcQ3215d/HZ0oaHhsRZL0TVajX5YMnHyCW9PM4c/weQ/FxEIK63pyeZR88D9Q5s/xHQeUtLS7LGjkm0cTeQRswAkvF7Wq8HsI/B2zZG4X4oZLclpKM0U8ZM37ljR2YMnjYe4T0OXsZAY9YNBULwNG+eeq02ZL/miCMyzyLZoXhzTF6hMR6/ds2a5HmimTvLly1DKE35hn50dDR5CL72d38HoLFZABHfaGGffWrL5O9loBnioYWh+AYAdE0LO1HFbG1ttW6aQDwkSxX+PTfdlLy1r73uupSMueCV+cjQ5ghS462NQKSMNPOa82sAP/npTzM6Hn/88ZSNv/+Hf8jI2YZNbHycf/PmzQCA5557TtwkwafH5QMC0zSAtxx9tDMvvjgtxNvOJapWq9bNOlpaWpI6SdcShfZ+SEdqJ6h6wy+Rb8ONJO9aJztM0XM2a9aslL+0wQYR/Z1LM4AoTiL6GSqE6O9cfk+5fckG35Ro33pPhHokEpGdyy6/PBXPF6+jcqE8UvtA8ePj440P5cwGxf/oxz9O6aY8SfXHd+9M2q2HbkKORWTL0FGGfd6VNM9DSOLncZIdigfy2XOVQ6ieENs2nbY8uXS74m1+uO6VTScvG8Aty3lCZfLWQ5efPj98Mq56F1I/Qu937H2Q4l12i7QHeXRAiFPTZeiG9owlUmgM3dAwDC842lP2yiuuSKZVUho/0jRLGpahH6ZouOYD739/ZuiGhmikj34KjaGcz9x5Z/IxloZsqPv3l1/+ckrmnr/9WygA9/7jP6byZHtw+EMUQwoQpzsqAKecfHLKr7POPDPZUCWUyNc3HXIIFIA3HHQQ3rZxY/QLyvQrlM8M+++3n1XWRIOmDLdJ8bZNLxQawx7j1SoUkBkmAbLTK2kIo2ogNQVklhOmj7XmT0/8hyjN/OmvVDDQ35/wm/aJl3/8Jft8gxDKi7TxSHdXl1hefDvAvt7eJL9cv/mxWSH70VgKtiWTSQctY0AfjSns3LEjdU9NGf7xlS9fzPOu0BiyoTgaujF/kDKvV61cmVk2gZed7ceptWvWJMM4xEsbmYTQSxbRExXR0WzZUFRpxofKxZQDINu1UVEdrjJw8RfV45IxH3ZbL4DSJB2+8pbigVrD19raitHRUbS3t2Punnui0tcHrTXmzp2L1ra2jDxtRDI2Nob5++yTq977/JTyZIuXngMXbyiiD73vIfUgRCbvM+x6Doo8h/WXxfRA9Oeec07yQxQh+WuuvhoKDeRO6UAt87RBwLXXXJPZjIS2EqRA10S8ID92++0JoqdFzfjHWLrmP0zd9dnPJkj+c3ffDYUGkqcjLYVAH2PpmpC95JtiR814TBkb8QpPcoQkTjv11KSS2Ro8TuQvfYxavGiRs5Hn+bP5lpcoTwe+/OUZW2YZmsH2w5Dkt3R/TFnzA6z0w5RpjwIh+r7e2m6dtLuUuZgY3ySE54l4zI21bT8q0ZHQL8+fuT2hlG8eZys7vsQCL2+fTonH1suiD650P2japfnDlE0n/yj76//5nwzPhlNOSdVzjsL5R1mSO3Lt2syCZ+ZWgeY19QLoo+zKFSuScwoxH2OnFaInMuNtshKvC8lNFqLnOujc9N1HoXwmryljkw8tyxj7XA8CZCX9efyXbAN2X2w80jWXLaKD8/h0UlxMr0fyIxTZ2vwJvQe2eFt9C8lLaK/EdZ33GTZ1IYAnpi1R0wXRc1JobEK96bzzoNDYSpDzSbLmuUKjd2Cmm29Nmz6Tl8bk6dqcXsl5v/D5z0MB+Mpf/ZVo9+//7u9S1yai5uja9LUsogp08kknefVKvQLp6COeh9iXg/kg2spDCUHioePoyAj0+HiCOKtjYxlUTnHEMzw0lPpRiX4wMn+I4qHS14fBgYEEMQ8NDiZj8JSOui6ztxGaL4qzjX2bZagYf1dnZ5IXc7ybzulIum1LD/t8leLNn72Ihy9axr+FPPa730Eh+x2A31szcJ/pRyoKJqI3NwpXaEyb5D8D0pEQvVQOfIkDCnyBNFq22JQlZD/txuiLvFFhSUOETq6DUx77of74yLRRhEeSie05SLJ55F26miXL7xlt9F2tVtFqTH+EwTdWn1eulEJbezvGRkcxWg97z5+P/koFbW1tGBsbQ2trK+bssUemXGg8XWuNvebNw9DgYLLxiLkImOsehtSh2GeGPrTOmTMn2SwcETpsz4HLR5efIfnM80yFyuaxL/EjgOcliejPOvPM5G1HCN78XV+hgejpRyla3Ozyyy5LfqIi5P7u669PvUFp1g39IEU6zcXN+I1QlnNC9rRM8R2f+lSyqNlnP/OZzFufdJv+0Ni8uXyxYjJ5yKwsofx5SKEx26ZsitEp8fLy5OWqUBv3BWpzoaUfhEzdY2NjGB8fT+ZJa60xPj6ezIFua2uD1jqZS859IDvVajVJHx0dTRYLAxrotL9SCf7ZSJrhQnGEvm2bhtg2Cx8cGMhsHkJHPr5PaNwcl+d55xudKCZL/OYsJKn8TN8f+e1vnXzmEgk8D9Rb4LI//MEPkvMHvvlNUS8dCcFzVC7VMz5jhwJfIoEQv/my4Sg/hF6yiL5MHURFZF06bCjEF2emmRR7x126Q/hj5U0ZXsmbWVv5qRlaUQAAIABJREFU/YJwLd1fn4wZZ7Pp0iHZD7Vr88Nlx5d/0/dQROt7Vni8ec1999m3keu5CfEn5Hkvqz1y2TeParogeqDxtqPf9Gn2DSF5Qvo0w4Zkrr3mmmRmDiF6Qvi0NIJtmWJC+Ld/9KPJrBtaxIzPsqHAly/+zJ13JouY3fGpT6XSaLliHiidj9XzB89WTtK5SRPxWjcrZlm6QoiXI4RrGy+XGR4aAtCY5z0yPJygS1pKeGR4GCPDw8lYsR4fx+DAQDIbhPY77a9UnHO/Tbs0ps/Hv02e/kol9c2A0La5jHBPd3eCdHt7epJ5+KS3u6sLfb29qfxJZWHmhY9982WTafEwmoNuljOf027mV9JpynV1dqK3pyejg/Iv2ZOCNOuG5tyTLB+zB+z3ihb34xuQELKnpYjNefQkS0idL3lA4/7EZ27MQ+fmZiShNIPoS9BBVIZ9SRcEPZxPunb5EUOSjGTHJueyGSKLAD0+CpG1lZ0NUQ/096OlpaW2JIdSGBsdxdjYGNra2tBWX6M9FA1T3ODAALTWGBkZwT4LFqR00LGnuxtz585FtVrFnD32wEB/P6rVKtrb25PrkZERtLW1Ya9582pDKPX1ZqrVKubvsw+2v/gi5s6dC6110GJjReuyT790TXltb29PvnPQIm0xSN5mi+fPpSvkHsaUS952YFojeiLKHCF62lqQFwwh+3ddeWWC4Gncnsbozc3BzSPFE4q/5X3vS7YTJJQvbS9oqyCE5GkePY3Z8+u/+su/TPQopBG9FnQTH4+TGhbN4rnveUjyh1+b442heqT00MbdLL+QdH40Hh5xcTF+TmPYtA6JUgojIyPJeiomSub2+Vg58bW1tWF8fDw1Pq7QQK9kh+bWA0jG80mGxv3N9VEoziTz24FtGWWyS2P3ClkETXmg8pOQv23Wj2srQSL6XsDLhC9u5gvPP/dcJo5kX9y2zXqvKN5cHM/kIwRP+ecbkPBxd/OceAn1awvfsqVLE9SvjbhQ2i0QPeBHQ1I8f4CbhehdfpRhPyTvlEYkoW1fuo0kHyDE+dBSiO4YP2LIVo4ufiLes5LifL0vXic4jy2d+xNqN4/veXtWIdeAX78LSZu+hsradIXeB1s7YLNfhg4eL/luxqnpiOiBWuZMJC+9nc0tBWmMntL4vHn6M5Z00fLFhOjfd/PN4rg9r4Cmf+bRxsO3FKQ/Ys359TFkVgwf2jYpb8PJ5SSbIevbSAimGcTt0N+yJpl1aWR4GHp8PBmXHx4aSsbkKY7LStekj1AurUHDNxYx16UBsvPeaf0ahdrfmzT/XkFecwZoIO+uzs5kK0K+Do3kq/RMjVeryRg5H8fm2xQC8v106XelA7UhHN7roJkyNM7uC79/+ulkC8Etmzfj6aeeSlA+2acyo+WLf/nww1Co9bCpF06BkDqNq9M6NqSLxvDpesXy5cn6NxRnLjls6ubLGR+xenXmz9iYefTehl4p9Sql1PeUUo8ppR5VSl1bj79NKfWCUurX9XCiIfM+pdTTSqknlFLrjfjj63FPK6VuDnHw1He8I7VWOmB0lRnvJRdfDCC9FKptKdFMPuvH/7+9L4/3o6jy/Z4kN4EAMyQhKAJPVheULQlhZwQdWXwaCBGSsEQEgsomgYRkGJbxjWASBMYnSQSSAA80YAKyOILo6PD0DSAgm6wJi8lj1ew3m0DNH7/+dlefruqu7t/v5t7c2+fzqU/9ajt16nR1/U6fOnWKbkHpNlVEYjey2u2odoXgWqi0qmbWzJkpXATSTNw8nDF3zpzCPw3fb187/QdZFLTvejjq6DwA6G9dTK1p9r3QeXT4xltU39UP3Sa78AKN52O7yKZ++2+RHr4spNQO0aUgur88oA4eaMzRXr16JSab4n7SxMn5S7fBVYHvEukIAUHjT+6vf/lLbr2VK1YU4tKqJ46rjzrn4KXFctfc1taG/v37Z+bo2mgDnfmMBw4cGLs/J5AP9hyxgSo83u3ar1+/eH3hE9NqNz02uy/fcw6BQtWNiGwHYDtjzJMishWAJwAcC+AEAKuNMVer+nsA+AmA4QA+BuBXAD4RFb8M4B8BLAHwBwBjjDHP+/pu1qkZ1O8yaTsO7V9DmbZ5MXH4wDdu3XdVKBpnq9ttDCh6Ztxo/fDDD9F/iy2wbu3a+HCUiKBfdCdCqDqCeFevWoVevXqhd+/eKRxFc8p+xdevWxf/2dibk76xUL++du1abLHFFti8f//SzyNvPoWkfTT62uT1r9uEtNXqmnfefrvxbPv3jx2/uXDmvYfNvMshzzukf2mV6sYY85Yx5sno9yoALwDYPqfJCADzjDHrjTGvAViIxqI/HMBCY8yrxpgNAOZFdXNh7Jgx8a1ENggSFY5Wz3AD1idpQv226/CuWLorvvyyy+JyuimmmSXzeZesDv923XUZs0ot4VNVM/ummzLSpaAh2fMQlY9210QxVtqlVikrG9h8DYVWL+5FfbvG5XvOvnLS3NbWhj59+mSkKPsgkW+DkZuTtjngmvZ2AIgXefsCDPt5s63epKQ7BQDxRqz9deqig1IyD2DZl+i46ucFmoq63il9yMnn4jcPv++GKQbbLYTvj8PX9te/+hUEDXXNn994A0Dj8pLVq1djTcRTvV7QFPP1116DoHFwav5Pf5oZlyA5wERXCDwMpVU7hxx8cMrtsB3TBTHD8P32gyBR4ew/fHhGdVMGSm3GishOAB4G8FkAEwB8DcBKAI8DuNAYs0xEfgjgEWPMbVGb2QB+EaE4yhhzRpR/CoD9jTHnqD7GAxgPAP8DGPpGlB/6r1f1H7QIp6sshI6i/ovocuFwgf3gXS9AnsTlw1c07iJ8ee2KeFmG1mbAR5sNWhq086pIcjYOFz0h/bpwFPWf128R5M2HkGfo67fMexlKe977Cbh56EoXSdRFY2jleuBrK63ejBWRLQEsAPBtY8xKADMB7ApgHwBvAfi+NW4NrvnG/HSGMTcYY4YZY4YNVo3p+kAPmq4P6K7YlvD1XbG6Y1GBkjyld1j1GHOjVg9u6ve+B0H6cBTb8AISHqDiZiwleeriuSnLcMe8efGlJLbJpQtCX2Dfw7DppSvWKviJg5uxrrKjjzoKgsYejM1b7se4+rSffV6/RfWK6vjGyXwekNJuBFImkFa8etWqVF0XHWLhsO+YXbtmDdauWYP21aszm8BacmYb5tsmnCtXrED76tWFkrMv2K4KSLvPvNGVzhuv3UYfirJxaLNNVx1B1oySG7CvvfoqHvrlL53PYfmyZfjLe+/FkjzfEZpdGqQW11TgJqzejNVmloccfHAs5dMkk5I76/DrgDH7HDpkSFyX8X7DCtf3ZIwhEr2ItAG4H8CDxphrHOU7AbjfGPNZEZkCAMaYq6KyBwFcEVW9whhzZJSfqueCZg9MVWnjioHsgtPREn0VHD4oKm+2ja9uHn2+sry6raK3CA+hSLLMo13/9rXReFevWhUfENpiyy2xpr09Vrn07dsXfdranH3YaT13eGDqgw8+QK9evbDlVlt53w8flHmGZaDo+ee9yz5cZd9loLHQt7W1ob29Hdt+5CNxvTJzr+i9zHuXQ3C52kqrJHppKClnA3jBXuSjTVrCcQCei37fC2C0iPQTkZ0B7A7gMTQ2X3cXkZ1FpC+A0VHdQuA/J10d6DJahZxy8smpckHiJoESPc0r7QtHBIluXl9E4qKDB6YYtJtigj1B6dSMEjwlerotttsKkotIbr/tNvyfW2+Nf/to03270mXANUHL4va9IIJE+tF1qi4YPsnfxRNRdXyg6+qXTddxSb38TWk0zzEZAIhIyqqHuvzevXunrM1c/cMRA4j3FezLvNmGjr5842VwSdNsU/UrwdWv3vew6WNdHw8XLVzoXDjpmuGVl1/O9PfYo48CAFauXAkRaZxyRiLJv7poUSF/qDvnnKbUzkNQDJ/7h3/IuDjQZpRMa4n/wAMOiE0w7a+AUAixujkEwP8F8CwA7v78E4AxaKhtDIDXAZxljHkranMJgK8DeB8NVc8vovxjAFwHoDeAOcaY7+b1valdDu7kX8m2re4fAeVF4KIRVl5I/4B7nJsKuMZgl7m+JIEsj1ILsAeHXa5f6BAe+uZQUb9V51DIswyZ50Vzu4jW0D5cbVx89/Xv67ej1yNXWwmU6GNf2F0xDAXMySefbE4++WRjADNu3DhjAHPqqaem0gysx3DmmWeas846yxjAfOtb30qVnXvuucYA5oILLjAGMBMnTkyVX3rppcYAZsqUKeaSSy5JlRnAfKhiXznDtGnTjAHM9OnTjQHMzJkzne1uvvlmb3revHmpsvnz52f6dAVXeWjehwW4i8oNYE477TQzfvz4FF+OPvroQrrLhiJe+J5NCD4frwxg1qxZ43yWrtDe3m7Wrl1rVqxYkcpfvny5MYDZsGGDMYBZv359qnzlypWZflatWuXsg7jb29szbZcuXZrbxhdWr17tLStq62q/bt26mDYDmLVr16bGxPoufr/33nuZZxLyDr799tte2hcvXpxq8+KLL+byy9XnoYceagxgDj/8cGMAc9BBB6XiAw880Bx22GGpusQzbNgwZz9sa4fhw4fHbQE8HrKWblIuEFrxL0goI1mH9t9M2zISTegYdD0XlPkqKYK8/gE37V0BqkikeWWu58/81atWxZeR5OnKi76iivoNfQ98bYvGmFdWhvYy75QeQ9k5VCTRh64lPrxdWaLfZFwgCBK7eW0/ry8Hp17+G2edlbG2YZ1vn38+BMC555wDQeKumME1IWiJYzs1EyQuERh4Unba1Kn4t+uuS9VlmtY3zGeal4Yzn7p6QfYyEtrX333XXZkxhoAAGHnccam2Ljyhi/yZZ5zhpUPjKPuClqnLkFfme9auueKbDwz6Sju6JgBSLyQAxM65eKqSZfaVheLowzc+O01dNFS5fVWhcbQTJPsL9hV+dvDpxQVZHb3W569pb8/UobWNttzRsWv8dLlAPXpooE28fvb2uEnPyy+9BEHiyOw/f/tbPPjAA85nQX06Y5+rYbtd0ZWBUPWH77dfbGtfBXqcRL+xcVRtS2gVjlCJvgqOojpV2upFqZlZ2kx7m/8+XHnPTJfbL2qehOuiI6Tf0C8qH/4qz7CIDlf/ofMs5F1q5RhC3uUqbWuJvgBGn3hixpoGaAySkr1tjSNInJqdNX58LLFTsj/n7LMhSCR534OjO+OrrrzSe80gY9rPF4EgORFLu/oZ118PQeKmWJ+QnTtnTizV0/8N4zvvuAMC4Kd33gkBcM/PfgYB8LO77/YuKBo4YShR7LP33hAAe3z608E4SOtJY8cG8cHV1paCWgGiYl0m6revb4P02F0Ssb4OL68fFx2C9LV74giuC6913aLLTfLGbDzlPl7llbnGFnpht+uCEg2kVeP8/0uWQJBYymj8vDzcFfQF52xnu2S2wW6rLx5hOU/I2qdhWTZs6FAIkHFyRqmdljwsHzZ0aMaOfuiQIR4OOeitJfruL9G7wNVPWRw+nD7cIf2GtOlqkEd7VRxly4vq+MpawdsQ2hHYT5kxlJ1LIbjz2sLTvjPXEukuEv3YMWNi3S9PTfLCcBcIGpK8oOGbhW19F41QVz/xoosgAC668MJU+cWTJmHyxRdDkNXRUzfPmD5wrr3mGggaOnp6uPz+1VdDkEjyGvjgKNmz/7lz5sQnY2lHT139HfPmperetWABBInO3g6aR5Tc7f41PWWBYxh94onOMQqQ8lskAI468kjny0WJJgRC69n18/hTFo+WpOH57UoX5YeW67mt6+sr+xh8J1HLhCLaVixfnnFtHIKriHf2XoggfS2gXXfJ4sXOd8CF2/fl8fRTT+HRRx5x4mGs3RVTwud1gQcdeGDmUnDGlOQZ25K8ABiy777xb7YZNnQoQqGW6DsYR2hbgitdtX8fTl9eXn4IhPTvK8sbQxFNVWhuZpxFuMrwW5d3BP/z+q8yd6rQgybwFOEvSofMqaLxh4yhM9YS6S4S/ZjRo2NdPJD+F9SSPnX5rP+1ceNi3TylfK2jpy5ex/R5c+GECXF/UyZPhiC5nETT44p5Ipa6eH0SlvkMtLphuHnu3FiCt0/GChKJnrp6WuFQshek6eFvqDxfXV+5rz2lc1127IgRXvxfOuYYCICvfPnLECRfGrRgEAA777STl04fPa60nbfNoEG54yzDFy0tu7xa0prGF3jtoLbgKRP0Bd95NDLQ2oSWLFrC1/sPZQJxAX5rnmaCHqetmxck0vmTTzyRil1tfSeE33rzzTj/97/7XW7/Pi+WlOgPO/TQjP8bfrlS725/ydrx/sOHpyxzGIdCmMf+TgQRQXvk4lUDv0Zuu/12AMnFCMzv379/7MqVZYzpfnbDhg2pNnTjyj4333zzuD9e2jB12rRgafzbF1wAoHHE2u5Hj4GgL3VYt25dXEdfRqBpHjN2bLocfmiFdKXHSl5p3D+75x6vNMkLXsgfpv/z4YfjNq+9/rqzvVFxUT7zBMkFM7a05Eq78Oox6Gfoutxj/fr16G/h4FyRnDY+2n2wbt069PXU0xfusA7z2X/oJR4hYM9lfWlIRwJ5zEvPeWHI4MGDPS2yz5DA9QEAtox8//uAc4qXl9juK4AGD3Q/+vKYRx591Pn8evXqFb8bVWCTUt0Q7JekK6pubDo7o/+iP6BmwYevTH4o7Xl4feUh4w3lSUjfCOg/RCgIVR2U5X9eWejcqcJTO112DlaZs0W0h/AnhP9F/W/MtUS6i+rGBn4i0ZyS7okZ6wNVQGJWyTrEw81ZOjljmqobbspePGlSrLKhmSUvGqF5pY/Oq6dPjw9IcYOWm7HM50UkdHJG1Q0vJJk7Z06MkzHdFVN1Q9UOL0ZYMH9+qn5VPvvK6GJYQ+iLKUhcGH/+iCNSk7oMDl8+TUPt4Gqr67iCNjN14TOqjcvMUfenVTQ+umh22b56ddzGd+GJHmNe/wzanFDXo8rHZ7ppB32nq43LR7MvFLkk9o3f5oNx5Pva0jRTH8Ki+unPb7yBPzz2WG7/3EDVF46QjsM/97mYBqpoqMphG5d6RtBQ7WiXxi13U9xZ0JM2Y4tiVGxTFvL61XV8aRculKTd1Ta0/1D6QqAKT/PalH1WeTwMoSGUxpBnWHUsrmdYBHn068U8FFcRzrLzsAqOqu+y7x2S7ibRC5LNVm6+fm3cOAgSCZ9p1v/mN74RS/f28XwBcP5550GQbM7SBQIlfKYnTZwYS/uU6Bnbl5MI0genmKZZJfMo0TPm5iwlej1mXkgiyG7G2lcMChKJngeoqoIA2GXnnZ04XDTaJmN2Pl1RfHXUqHhDVrtc4IT9xy98ITWWvffaCwJgxx12wPYf+1juy+1Lk2+uBcoXoH6HgguHpkOQbG7adULoybvKzxWK8PKCDeLybbqy3CVh218bdlrTt2zp0tKujPPMPte0t2NNe7vXLYN+Li++8AIEwLPPPOPF+c7bb6di/ZyWLF6MJx5/3Mt/uz9uxn7h85+HIJHamW/XZZm+aIRp29xSb9QesP/+CIVaou9gHK3ov1mJXqfzIGQMvjZ5uDSOUJrttq2EKjwK5WNevdB+m3mGVfotwu8qL3qGdj0U4PfhdJWvaW+PL1LZcqutCnHnzaUq73IRzWVwNNtWuotE/9VRo+JDNjwoxYEzTamdF5BQkjzzjDNiHT3dIvCCa0r0PDClY5pQTrjggsyBKR0DSMXfu+oqCIDp06bhyu9+F4JER09zS7pC0OaWP5o1C4JERz9n9mw+0PgwFfX4lPDpEoFmlbaO3gZXngbylhK13ZYmj642ebhGHX98LNHTnJISPPWaWgLtSPFDAAzeZptUfzoNx2/fS67r6+vxfHj1uG2ctk7c12dIYH0tUWs8RXhdEjmlfILvK8L1ZeML3DPw6egBZCzp7LouXvEqwcV//jP+9NxzqfHyy4YO4SjR09yUZqHvvvNO/GWg8duSup2mWSXzDzn44NwLvgVZ1whD9t03M/dC3uMMH2qJvnU4CJ3VfxEOG8pIr0XjzWvLdNEYQmlpFfiema9emTG76lbhWUdK9K0oD6XJfv5F+PPeKSA7n4uk/46UqJvB0ap1QLqLRD92zJjMgSj7iL1vEWM+JXnW1a4QqKO3cQkSHf3kiy+O3SOwjC4Q6N4AVpmLHkEi0TNNHT0le0rymo4f3357xtEZrxZkmhI9dfOU7OnkbMH8+bj/vvti3uQBx/CpT37SOR5BVn/vGzfLjh85Mv7aorTDOISeqiBw0+biMw9Q6XY7ffzjmXwX2Iu8S/rVeIvKM2ljnG3y8CKwXt67xHKXDr8IJ8OqlStjidnXlpKzPsBl1+el3+S174AY09qdyP/7/e8zbkMYP/fss858fi0sX7Yslug1T+nygF+8WsK3D0lxbbJdF9ttmE+J3nZ6xrpDhwwp/V50C4k+D3xtinDY5UX958VV29p0NNN/qDTSzPjz2hbhKII82kOhzDyp0jakjWvcvt8+nEA1HobSm4eval/2glR2zhR9QYTwzPcuFdV10QW4adsY64GvrXQXiZ7g+relZO/6N/fhoOsDxpToXW0EDcmeEj2tbWg/T2ZfcfnlECQSPp2clRmTvnjE9YLQAke7RNCuEJi+9557IGhI9vffd18pPlEK2WvPPeOdf9JBiUI7RtP0Uh9v51H3H/plEVrPly8ABg4YkKmj542vH+uF8rbNo4vBvmDDR7/r+bj6ZmyQ7cfXpqie7lPTWOUrhXXWrV2bkoxDaHBZ1Lzy8steeotwEoxqZ+cXPVfq+nX/tJdnTKlcS/ZHHH6482Jv+33TTs18l40IurFTMw36BbUnf5FE5Ytd9RHY1hU307YjcLigiF9F/fsgD0cZqNLG1973nFtFQ9F8Kkr7npXOL3qmPrryygmt+rJx4Q2dQ3lzUqfLzMPQL7sy79DGepddbaW7SPRjx4yJrW58/7b6H5xWOHbgtYNkEHX3tL6h1M5Aq5uJF10U42XsOhFrl0+fNg2CxqUltKOfNnVqqg6tb5jmCVmmqZe/ee5c3Dx3bqpM289rt8XU1TO++667YoneppXgmrzkky2F2JJ9CBDH/sOH48gvfrHpBTsE9PwQJBK965nZuvlW9M3x+SRH79yNdPDQvlAsvJp3ttWLnu+ud8X3/G16QyCvL987yi8a+yIPgd/23tVPpk7EM/uUsQvyBBwdnnn6aQiSE7J57Wz8guSUNyV7Wt3w/dl/+PBYcrct2ATJSVnazzPm1/Pw/fbDvvvsk8LXbe3oCfaC3RUk6jy6OrL/MvzQOKDaw5Gvy3z9FkEe/hBo9g+iVWMIlQZdeXllIfUIVWivAq3GXTRHq9BRhKPMs27mHdpY65GrrXQXif7YESPi3/wn1dY3lOA5eNsXTt6F1YKs1Q193VDCn3jRRbFunvb0+iEz0AcO84HEpp55+vSsPhHLMdCO/qYbb4x18bSpZ5qBkjxjXiXIC0juvusu/PvPf+7lg0tatMtIK216qyzcrVo07P7t365ypgcNHBhL9bZ0zzKNTxx4XM9cQ94YixYzHbtocOF00ewbh4tXvvyXXnwxt28X5PWvzxbQL05RO22t4xuTfuaCxmUhvvmqcbzx+usQJD5v6LaY8VtvvhlfRajHTKsbSvCUzqmjt68YJPDZ6SsDGWufN/bl4GUu5SF0+YV+s802A9AY7IknnAAg6/6TByj04Hv1SoZHf/T27VNAcuji/PPOA5B1p9rW1oZ169al+vO9gHQ16oLrf/jD1Hiuu/ba1FhoZkkMzBeRuH+2ff/99wE0nJvZdbXbYtsFbVzHQ79rAWUYsu++ABJeffpTnypcVIogtL1vUS/qJ/WCW1+t9pxw9ZOHR9crs8C68NgLD10c5IFWB5V52X20uMYDAM8//3ymna5TBjhnCaGaBN0uDzRtfA94w5TvOQCJe+wtttgCQPLubBWdvO3duzfWrFnj7IvrAt9TAl0ccwwHH3RQRljS4+P8JO1777VXTA9dSBPv/sOHeziRhULVjYhsBuBhAP3Q8F8/3xhzuYjsDGAegIEAngRwijFmg4j0A3ArgKEA/grgRGPM6xGuKQBOB/ABgPOMMQ/m9b2pHZjqiv2HqG5cUKX/Ilz698aCkDFsDBpCeOSr58ovk4eC/ov6y6M/ZGy6fz2/8sr1PNR4ATfvyjzrMu9QlbZ5Y+gqqpv1AI4wxuwNYB8AR4nIAQCmArjWGLM7gGVoLOCI4mXGmN0AXBvVg4jsAWA0gM8AOArADBEpvG1hzOjROPGEEyBI7htlfNLYsSlJgyob200xy844/XQI/M7NdJoHqyZNnBiXcYOWt0/9yxVXQJC4QqDqhuqa7111VWxySVcIxMXNWe0KQbtEuOnGG2OzSh1zE5Z1aV7JtIYyi7z96ci8z37mMxAkbor5mbnXnns6++sIiCZ3/LtMHQEyKhzfQSlBFr/G5eOxr8wHdv8+d8Au+lymipo2TXMo6K+HkLp2P3ZYvWpVxjzTdoS2csWKOM32+j7YlStWOF0qA8lmrHb9zMUwb2OVuLSTN24e23QtWrjQyWsdU1VjH3ZyzR1Bog710UUT5v2GDcvg45d2CJTajBWR/gB+B+CbAH4O4KPGmPdF5EAAVxhjjhSRB6Pf/yUifQC8DWAwgMkAYIy5KsIV1/P1V0v0rcPhkqjt3xqq9B+Co5WQR7+ux/5Daa/aV7Pty/DKN1dsCOV/UZ1QHHlSu0saX9Pejvfffx8igq3+7u+8czOv/zLzsKpE76KpLA4XrmbXAWnlZqyI9BaRpwC8C+AhAIsALDfGUMG0BMD20e/tASwGgKh8BYBBdr6jjd3XeBF5XEQefw8Nh1ijjj8egsSJGaJB0i0xJXvXxSMp3EgOShHIMEryjO07ZP9pyhQIEMd0j0BJnpu1LqBEf833vw9BdjOWrhBoXqkl+5kzZmQuI2Fb3x2yNKukczN7cvp+ayBf7E3eoX3fAAAPbUlEQVQg+9i2rhsKPj5VbaPLROUJGhuu3HTVcd6L75tDvuAqZx7ncB4IGpuURf0waDfBGowVhzyjojplygVp80/tpCxuY0zqGkUfL12gn7PvXl5XO91HHrCOlvp10PfBMtbvjSB7oY3mHU0p6eRs6JAh8W9+BXTYgSkR2RrA3QAuAzA3Us9ARHYE8O/GmD1F5E8AjjTGLInKFgEYDuA7AP7LGHNblD87arPA1193luhjnm6EMTQDIf3ntfWNwVc3r/+QfB8duv9W8KYMlOFVKF2+uYISODoS7LHkzX9CEc3NvMOhXyJ5bcs+w42xlkhHmFcaY5YD+C2AAwBsHalmAGAHAG9Gv5cA2BEAovK/B7DUzne0KQ2uf059XaCrLg9O0ZUxJXztvpjmlZMmTozr0LySunoymzp7SvxXXXklBA0dvtbN07mZqDSvFuRBqhnXX595uLaTJntst95yCwTJQSot2es2dltfGcGe5NrBUhGUlSiLQC8KesFw0SZo6OG1GaWd1vV9IY+uEL5oXHba5lUoPfrAVFHfnQ0C93WDdnkR/wVudxKC5MuGMU0mAf8z8uXRzJKO0wT5En0rwDcvBQ3p3XY5ImhI/cG4A6xuBgP4mzFmuYhsDuCXaGywjgOwwBgzT0RmAXjGGDNDRM4GsKcx5hsiMhrASGPMCSLyGQA/RkO6/xiAXwPY3RjjvR7eJ9HbDNhUJfqNicMFeWW+umXa6Pbw4CjCbbetCnk8LIOjWRpCvz7KPhc7TXD9YYRKtc3S5WoT8pzz8pt5D0JpJX4fvaE8auW77KLPriMtlOi3A/AbEXkGwB8APGSMuR/AxQAmiMhCNHTws6P6swEMivInINmE/ROAOwE8D+ABAGfnLfKEkccdl7GuYax19trq5uunnRbn8WAUJXlK8DpNif7CCRMgaEj2vCjcdl0sSNwV6ysGqYefNnVqbF1DKZ8PiK4RWJfWN9oK54Yf/SjW0TPmeGl1c8vNN6fyaX2jJXqxgg1F5RqKyqu0C/ls16EM+BbE0L7ohiKk/7L02fVD/wxYV7e1F6VQGgSJftk1RkH6wvW8MbjGov+AXP34rjKkJY2rP8aUunn9IPuwnaAVQSjfXDQyaAsupu19Id9eEa3AdNpHj6BhkRMKm5QLBKkQN9O2IyR6wsYeQ1npxIY8PKHlgLu/IjryeBgKVdu1GkdH959Xx/fMdNqHo+rcKaItdG6VwVHUZ7O0FrXT7y4caVeeL523Dkh3cYFAEDRs6gWJBQPTp55ySip2Wd2QMbxakDFxUw9PSZ75dv+U5Jmmbl67PrDdHjDPtrax6aFOnmla4bDdjTfckHKTYMe+C0iY5mXhdp/6t0taYD4DLQB4xLuoLUqUbzt4cGE91qV9MkMrIQ+nbz6E0l0UQiBkwaqy+DJw/tGNtI9GAbDrLrt4y8qMSZCcGzAffhjMTx/oean5EcL7MvNK46IEz2spKcFrid6XZ6e1hG+Pjbr6oUOGhNNaS/QbV6JvBQ5CFRxVQOMrKi/Tvw+nXc62RXXzcHS2RN8KGlw4geZ4Y+MqmmPN9KUXK/3bXmBDv0pC+ipbp8y7UsSrQQMHxmV/Xbo0lWbewAEDICIwxmDpsmVxmuU2La73SrqLRH/8yJE4fuTIjFQlyF4pyCsHKdmfcfrpsXTPk7Gsqy107BOysPIvnjQplvZ5cTh187Syodti2sxTL3/pP/9zrJvX1je0q6e1jR4brW5sHT3t6EXVZUy7eurmNZSRYPSLJ2i4LNb9uyBUbZOX73oWjPP6LtOHD5+Lx7zEpWzfITTk0VKE01i/q+IUpK+ns8f/yU98ItVekFwlqfN10LSynv37bxs2VOZpVdDvjg3kqW9euHAxUKLfZtCgcFoc/rGMMbEvIEr0Ns2kb79hhet70k8t0W96En0rcBB0GjllLnyh9UOkdhe0QlptBVShoSvQ7YOiedCqOVRUR+OFlS4LIfMsFK+rbpl57BqP68+vShu7P+kuEv2Y0aNj6xrq5un7RuvoKeFTsv/6aafFbenjhnb0tMJhmlY3PBELNBh54YQJcV19MpY6eluvDyQP6Lv/+q+xvp4SvT4ZSzv6q6dPT+VTip81c2bG2kbH2veN67Jw/r7v3nuDJBWOwy7nuM4aPz7TXpDod3V7o+rZEkq2U5OEvHpw8yKvXh64eJJLZwA+zq2q7au29eGzf9unOHWfguREJv0b7bbrrrF0v/tuu5VelEO+6IwVbHpaBWX+PMrUJZ2h+ve8OgMHDMi41WaeLd2XFSJqib6DcZRtS3Dh0NBKOvLAR1vVeq52MdjzUSSXxtAx5PFQ46pa3qo2HYWr6Nnkzb+OmEMbEzq6b/ImTyfv09HrOi6dfdyPSEpvHy343UOiP+boo2NvlVon/9VRo5xSHSX8r40bF/8+79xzIUhs7VmX+ndthUN9/IUTJsSnZOnRUnuxpI5e29NfdeWV8cXh1NXri0h0zEAd/ayZM/GjWbNSZQy07dZeLPVl4fN+8pPY7w11zQ8+8AAEyFxIoqWovDJdntdGBx++PFwuOvJ4yDBo4MDYEsIXqF/1Be675I2jTFlR0GOlZF21D11P46dllY+3u+26ayaPunpK+iH9636Rk1eVd3lzJm/O+crzeMi5AyCjb68qRPvaMd+2yAmFPsVVOhfa2toyF2oQeDkAkFUzAI0LAejYX1/eQaCjf/2Q2Ffv3r3j39dedx0AxBcAMJ+XehCIa8OGDdh8881TZbzgIE8vDiC+5KBPnz6ZcWsYe9JJKamL/TPu27dv/Ju41kYXXRzzpS9lPp/hSIuHZl+e72UiFJUX4S+qL1ZaWy/k0eKj3b6wxMejIpyh+l2NWwC8+WbWW4hWjeXhLVKd/PGpp5zvEPMWLlqU4Q3nEi/CKIK8eebKc+msfRA6zjwIVS9520frlL1Qhy72dj1K9GXaFEGtuulgHL62PujsMbjogFUmnt+uMg0hfwC+NkV/OC4I5XlI3ao0tAqKFrKN0b8efxn+tpqGKu1c70Er6NH4EZAOqZPXptupbsaMHh2raI4dMQKCxC0xY94hS7fFdkzzSntjzE4zdplX2uD6nJsyeXIqtusJgCsuvxz/6zvfSeXRBNPGY4eZM2ZAkLhCmDljRuYyktk33QRB4syMLhB4gIqqHKpwFsyfj7sWLIAguUeWuPLuknVBpZcj2lzVi5Wxgi+t+WovNrqsiGa7jauOAF4Vj92e/C3Dt7J/cLrtvvvsg2OOPtpLWyv7d+HVz12Q3ZR1tW1m3D5ayoy52f5D+rA3ToG0GwNupAryN2NtdYxrM5bB7q+M6qaW6DsYR2f3XxVHEeiFl3khEn2ZGWfjcX1h6H5127KzOwRXK/GWaQ9Ul0bLPl9f+6K8VoOL776xhOQDrafZ977ZG6n2YShjTLyxysXad2DKxkE8HEP0R9Y9JHoNgmQTlpuz9uarlvJYRudldpkLN80ruRl7ztlnx5uwrENzSm2KyYNUjC+79NLYPQIleZpXTp82LRXz4BRdIFCK/98/+EG8GUtXCExzM5bui+mmmK4PaFJ5x7x53kNUevzcpLWloZB29iTP1FcSfRnQL6zOc6WL8m18otKU6HUdmrD6Fry8OSUAvvLlL5caf94f5cjjjgvC5XqOtiPAZiGEv5qWUNB067YCYLuPfrSQ7/a8/Mi221aiJY9GIL24F+F1CdZc+O0067kOVOkviCBau7JELyKrALzU2XR0EdgGwF86m4guAjUvGlDzIYGeyouPG2MGF1Xq6lY3L4V8lvQEEJHHa140oOZFA2o+JFDzIh82OdVNDTXUUEMN5aBe6GuooYYaujl09YX+hs4moAtBzYsEal40oOZDAjUvcqBLb8bWUEMNNdTQPHR1ib6GGmqooYYmoV7oa6ihhhq6OXTZhV5EjhKRl0RkoYhM7mx6OgJEZI6IvCsiz1l5A0XkIRF5JYoHRPkiIj+I+PGMiAyx2oyL6r8iIuM6YyzNgIjsKCK/EZEXRORPInJ+lN8TebGZiDwmIk9HvPiXKH9nEXk0GtcdItI3yu8XpRdG5TtZuKZE+S+JyJGdM6LmQER6i8gfReT+KN0j+dA08BRWVwoAegNYBGAXAH0BPA1gj86mqwPGeRiAIQCes/KmAZgc/Z4MYGr0+xgAv0Dj8N0BAB6N8gcCeDWKB0S/B3T22EryYTsAQ6LfWwF4GcAePZQXAmDL6HcbgEejMd4JYHSUPwvAN6Pf3wIwK/o9GsAd0e89ovemH4Cdo/epd2ePrwI/JgD4MYD7o3SP5EOzoatK9MMBLDTGvGqM2QBgHoARnUxTy8EY8zCApSp7BIBbot+3ADjWyr/VNOARAFuLyHYAjgTwkDFmqTFmGYCHABzV8dS3Dowxbxljnox+rwLwAoDt0TN5YYwxq6NkWxQMgCMAzI/yNS/Io/kAPi+Nc/MjAMwzxqw3xrwGYCEa79UmAyKyA4AvAbgpSgt6IB9aAV11od8ewGIrvSTK6wnwEWPMW0BjAQSwbZTv40m34lX0yb0vGpJsj+RFpK54CsC7aPxZLQKw3BjzflTFHlc85qh8BYBB6B68uA7AJAC8eGIQeiYfmoauutD7fDr1ZMjzc+XK3+RARLYEsADAt40xK/OqOvK6DS+MMR8YY/YBsAMa0uenXdWiuFvyQkT+J4B3jTFP2NmOqt2aD62CrrrQLwGwo5XeAUD2ip3uCe9EaghE8btRvo8n3YJXItKGxiJ/uzHmrii7R/KCYIxZDuC3aOjotxYR+qayxxWPOSr/ezTUgZs6Lw4G8BUReR0N1e0RaEj4PY0PLYGuutD/AcDu0Q57XzQ2V+7tZJo2FtwLgNYi4wDcY+WfGlmcHABgRaTOeBDAF0VkQGSV8sUob5OBSJc6G8ALxphrrKKeyIvBIrJ19HtzAF9AY8/iNwBGRdU0L8ijUQD+wzR2Ie8FMDqyRtkZwO4AHts4o2gejDFTjDE7GGN2QuP9/w9jzEnoYXxoGXT2brAvoGFZ8TIa+slLOpueDhrjTwC8BeBvaEgep6OhV/w1gFeieGBUVwBcH/HjWQDDLDxfR2OTaSGA0zp7XBX4cAgan9PPAHgqCsf0UF7sBeCPES+eA3BZlL8LGgvUQgA/BdAvyt8sSi+MynexcF0S8eglAEd39tia4MnnkFjd9Fg+NBNqFwg11FBDDd0cuqrqpoYaaqihhhZBvdDXUEMNNXRzqBf6GmqooYZuDvVCX0MNNdTQzaFe6GuooYYaujnUC30NNdRQQzeHeqGvoYYaaujm8N9/FRmeoJIBpQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "frame_partition(seq_name,\"CTU\",img,yuv_img,QP,currframe = 0)\n",
    "\n",
    "\n",
    "#frame_partition(seq_name,\"TU\",img,yuv_img,QP)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "def frame_partition_saveimg(test_seq,unit , img):\n",
    "    frame_width , frame_height = img.shape[1],img.shape[0]\n",
    "    img = np.zeros((frame_height,frame_width))\n",
    "    fig,ax = plt.subplots(1)\n",
    "    ax.imshow(img)\n",
    "    for i in range(7,8):\n",
    "        f = open(\"D:/frame/\"+seq_name+\"/\"+QP+\"/\"+unit+'_%d.txt'%(i), \"r\")\n",
    "        for line in f:\n",
    "            row = line.split(\" \")\n",
    "            startx , starty , height , width = int(row[0]) , int(row[1]) , int(row[2]) , int(row[3])\n",
    "            if startx == 0 and starty==0 and height == 0 and width == 0:\n",
    "                continue\n",
    "            rect = patches.Rectangle((startx,starty),width,height,linewidth=1,edgecolor='r',facecolor='none')\n",
    "            ax.add_patch(rect)\n",
    "    plt.savefig(unit+'_output.png')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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ZZcwhYGaWMYeAmVnGHAJmZhmr509Jz5b0lKSdknZI+myqT5fUI2l3up6W6pJ0n6Q+SdskXV76WSvS+rslrRi7l2VmZvWoZ0/gKPC5iPgAsBC4TdKlwEpgU0R0AZvSbYDrKCaT6QK6gfuhCA3gLmABMB+4qxIcZmbWHsOGQETsj4jn0/JbwE6KSeKXAWvSamuA69PyMuDhKGwGpkqaAVwL9ETEQET8AugBljT11ZiZ2YiM6JiApAuBDwNbgAvStJGk6/PTajOB10sP60+1oerVnqdbUq+k3iPFlMVmZjYG6g4BSWcD3wHuiIhDtVatUosa9VOLEasiYl5EzJvI5HpbNDOzEaorBCRNpAiARyLisVQ+kIZ5SNcHU70fmF16+CxgX426mZm1ST1nBwn4JrAzIr5eumsDUDnDZwXwRKl+czpLaCHwZhouehJYLGlaOiC8ONXMzKxN3lXHOlcCnwBelLQ11b4AfAVYL+lW4KfAx9N9G4GlQB/wNnALQEQMSPoy8Gxa70sRMdCUV2FmZqOiiKrD8h1jiqbHAi1qdxtmZuPGltjEoRiodhz2FP7GsJlZxhwCZmYZcwiYmWXMIWBmljGHgJlZxhwCZmYZcwiYmWXMIWBmlrGO/7KYpLeAXe3uo07nAv/T7iZGYDz1O556Bfc7lsZTr9Cefn83Is6rZ8V6/mxEu+2KiHntbqIeknrHS68wvvodT72C+x1L46lX6Px+PRxkZpYxh4CZWcbGQwisancDIzCeeoXx1e946hXc71gaT71Ch/fb8QeGzcxs7IyHPQEzMxsjHRsCkpZI2iWpT9LKdvdTIWmvpBclbZXUm2rTJfVI2p2up6W6JN2XXsM2SZePcW+rJR2UtL1UG3Fvklak9XdLWlHtucaw37slvZG271ZJS0v33Zn63SXp2lJ9zN8rkmZLekrSTkk7JH021Tty+9bot+O2r6QzJT0j6YXU69+k+hxJW9J2+pakSak+Od3uS/dfONxraFG/D0l6rbRt56Z62z9rNUVEx12ACcAe4CJgEvACcGm7+0q97QXOHVT7KrAyLa8E/i4tLwW+BwhYCGwZ496uAi4Hto+2N2A68Gq6npaWp7Ww37uBv6qy7qXpfTAZmJPeHxNa9V4BZgCXp+VzgFdSTx25fWv023HbN22js9PyRGBL2mbrgeWp/gDwmbT8l8ADaXk58K1ar2EMtu1Q/T4E3Fhl/bZ/1mpdOnVPYD7QFxGvRsRvgHXAsjb3VMsyYE1aXgNcX6o/HIXNwFRJM8aqiYh4Ghg8ZedIe7sW6ImIgYj4BdADLGlhv0NZBqyLiMMR8RrF9KXzadF7JSL2R8TzafktYCcwkw7dvjX6HUrbtm/aRr9KNyemSwB/DDya6oO3bWWbPwoskqQar6GpavQ7lLZ/1mrp1BCYCbxeut1P7TdwKwXwfUnPSepOtQsiYj8UHz7g/FTvhNcx0t46oefb027z6srwSo2+Wt5vGn74MMVvgB2/fQf1Cx24fSVNUDGH+UGK/wz3AL+MiKNVnvdET+n+N4HfblWv1fqNiMq2/du0be+VNHlwv4P66oTPWseGQLW5MTvlNKYrI+Jy4DrgNklX1Vi3k1/HUL21u+f7gYuBucB+4J5U74h+JZ0NfAe4IyIO1Vq1Sq0T+u3I7RsRxyJiLjCL4rf3D9R43rZv28H9SvogcCfwe8AfUAzxfD6t3vZ+a+nUEOgHZpduzwL2tamXd4iIfen6IPA4xRv2QGWYJ10fTKt3wusYaW9t7TkiDqQP2HHgQU7uzre9X0kTKf5DfSQiHkvljt2+1frt5O2b+vsl8F8UY+dTJVX+tE35eU/0lO7/LYphxZa/d0v9LklDcBERh4F/psO27VA6NQSeBbrS2QGTKA7+bGhzT0h6j6RzKsvAYmA7RW+VI/srgCfS8gbg5nR2wELgzcrQQQuNtLcngcWSpqWhgsWp1hKDjpncQLF9K/0uT2eGzAG6gGdo0XsljTl/E9gZEV8v3dWR23eofjtx+0o6T9LUtPxu4KMUxzCeAm5Mqw3etpVtfiPwwyiOtA71GppqiH5fLv0yIIrjF+Vt23GftRNafSS63gvFEfVXKMYGv9juflJPF1GcffACsKPSF8V45CZgd7qeHifPIvhGeg0vAvPGuL+1FLv4Ryh+y7h1NL0Bn6Q4qNYH3NLifv8l9bON4sMzo7T+F1O/u4DrWvleAf6QYld9G7A1XZZ26vat0W/HbV/gQ8BPUk/bgb8ufd6eSdvp28DkVD8z3e5L91803GtoUb8/TNt2O/CvnDyDqO2ftVoXf2PYzCxjnTocZGZmLeAQMDPLmEPAzCxjDgEzs4w5BMzMMuYQMDPLmEPAzCxjDgEzs4z9P7QESFdl0/uAAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "frame_partition_saveimg(seq_name,\"CTU\",img)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Eliminate the samples whose RD cost difference between the optimal and sub-optimal partition structure is too small."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "def RDeliminate(test_seq , QP = \"QP\" , ratiod8 = True , beginx=0 , beginy=0 , i = 0):\n",
    "    qt64_32 = []\n",
    "    checklist = [False]*16\n",
    "    costlist = [0]*16\n",
    "    costlist_32 = [0]*16\n",
    "    \n",
    "    costqt64 = [0]*4\n",
    "    costqt32 = [0]*4\n",
    "    checklist64_32 = [False]*4\n",
    "    f = open(\"D:/frame/\"+test_seq+\"/CURDCOST/\"+QP+\"/CTU_%d.txt\"%(i), \"r\")\n",
    "    for line in f:\n",
    "        row = line.split(\" \")\n",
    "        startx , starty , height , width,cost = int(row[0]) , int(row[1]) , int(row[2]) , int(row[3]) , float(row[4])\n",
    "        \n",
    "        if (height == 64 and width == 64) :\n",
    "\n",
    "            if (startx - beginx)//64 == 0 and (starty - beginy)//64 == 0:\n",
    "                costqt64[0] += cost\n",
    "            elif (startx - beginx)//64 == 1 and (starty - beginy)//64 == 0:\n",
    "                costqt64[1] += cost\n",
    "            elif (startx - beginx)//64 == 0 and (starty - beginy)//64 == 1:\n",
    "                costqt64[2] += cost\n",
    "            elif (startx - beginx)//64 == 1 and (starty - beginy)//64 == 1:\n",
    "                costqt64[3] += cost\n",
    "\n",
    "        if (height == 32 and width ==32):\n",
    "            \n",
    "            if (startx - beginx)//64 == 0 and (starty - beginy)//64 == 0:\n",
    "                costqt32[0] += cost\n",
    "            elif (startx - beginx)//64 == 1 and (starty - beginy)//64 == 0:\n",
    "                costqt32[1] += cost\n",
    "            elif (startx - beginx)//64 == 0 and (starty - beginy)//64 == 1:\n",
    "                costqt32[2] += cost\n",
    "            elif (startx - beginx)//64 == 1 and (starty - beginy)//64 == 1:\n",
    "                costqt32[3] += cost\n",
    "  \n",
    "\n",
    "           \n",
    "            ######################################################################\n",
    "            if (startx - beginx)//32 == 0 and (starty - beginy)//32 == 0:\n",
    "                costlist_32[0]+=cost\n",
    "            elif (startx - beginx)//32 == 1 and (starty - beginy)//32 == 0:\n",
    "                costlist_32[1]+=cost\n",
    "            elif (startx - beginx)//32 == 2 and (starty - beginy)//32 == 0:\n",
    "                costlist_32[2]+=cost\n",
    "            elif (startx - beginx)//32 == 3 and (starty - beginy)//32 == 0:\n",
    "                costlist_32[3]+=cost\n",
    "            elif (startx - beginx)//32 == 0 and (starty - beginy)//32 == 1:\n",
    "                costlist_32[4]+=cost\n",
    "            elif (startx - beginx)//32 == 1 and (starty - beginy)//32 == 1:\n",
    "                costlist_32[5]+=cost\n",
    "            elif (startx - beginx)//32 == 2 and (starty - beginy)//32 == 1:\n",
    "                costlist_32[6]+=cost\n",
    "            elif (startx - beginx)//32 == 3 and (starty - beginy)//32 == 1:\n",
    "                costlist_32[7]+=cost\n",
    "            elif (startx - beginx)//32 == 0 and (starty - beginy)//32 == 2:\n",
    "                costlist_32[8]+=cost\n",
    "            elif (startx - beginx)//32 == 1 and (starty - beginy)//32 == 2:\n",
    "                costlist_32[9]+=cost\n",
    "            elif (startx - beginx)//32 == 2 and (starty - beginy)//32 == 2:\n",
    "                costlist_32[10]+=cost\n",
    "            elif (startx - beginx)//32 == 3 and (starty - beginy)//32 == 2:\n",
    "                costlist_32[11]+=cost\n",
    "            elif (startx - beginx)//32 == 0 and (starty - beginy)//32 == 3:\n",
    "                costlist_32[12]+=cost\n",
    "            elif (startx - beginx)//32 == 1 and (starty - beginy)//32 == 3:\n",
    "                costlist_32[13]+=cost\n",
    "            elif (startx - beginx)//32 == 2 and (starty - beginy)//32 == 3:\n",
    "                costlist_32[14]+=cost\n",
    "            elif (startx - beginx)//32 == 3 and (starty - beginy)//32 == 3:\n",
    "                costlist_32[15]+=cost\n",
    "    f = open(\"D:/frame/\"+test_seq+\"/\"+QP+\"/CTU_%d.txt\"%(i), \"r\")\n",
    "    for line in f:\n",
    "        row = line.split(\" \")\n",
    "        startx , starty , height , width = int(row[0]) , int(row[1]) , int(row[2]) , int(row[3]) \n",
    "        if startx == 0 and starty==0 and height == 0 and width == 0:\n",
    "            continue\n",
    "        cost = float(row[7])\n",
    "        if (startx - beginx)//32 == 0 and (starty - beginy)//32 == 0:\n",
    "            costlist[0]+=cost\n",
    "        elif (startx - beginx)//32 == 1 and (starty - beginy)//32 == 0:\n",
    "            costlist[1]+=cost\n",
    "        elif (startx - beginx)//32 == 2 and (starty - beginy)//32 == 0:\n",
    "            costlist[2]+=cost\n",
    "        elif (startx - beginx)//32 == 3 and (starty - beginy)//32 == 0:\n",
    "            costlist[3]+=cost\n",
    "        elif (startx - beginx)//32 == 0 and (starty - beginy)//32 == 1:\n",
    "            costlist[4]+=cost\n",
    "        elif (startx - beginx)//32 == 1 and (starty - beginy)//32 == 1:\n",
    "            costlist[5]+=cost\n",
    "        elif (startx - beginx)//32 == 2 and (starty - beginy)//32 == 1:\n",
    "            costlist[6]+=cost\n",
    "        elif (startx - beginx)//32 == 3 and (starty - beginy)//32 == 1:\n",
    "            costlist[7]+=cost\n",
    "        elif (startx - beginx)//32 == 0 and (starty - beginy)//32 == 2:\n",
    "            costlist[8]+=cost\n",
    "        elif (startx - beginx)//32 == 1 and (starty - beginy)//32 == 2:\n",
    "            costlist[9]+=cost\n",
    "        elif (startx - beginx)//32 == 2 and (starty - beginy)//32 == 2:\n",
    "            costlist[10]+=cost\n",
    "        elif (startx - beginx)//32 == 3 and (starty - beginy)//32 == 2:\n",
    "            costlist[11]+=cost\n",
    "        elif (startx - beginx)//32 == 0 and (starty - beginy)//32 == 3:\n",
    "            costlist[12]+=cost\n",
    "        elif (startx - beginx)//32 == 1 and (starty - beginy)//32 == 3:\n",
    "            costlist[13]+=cost\n",
    "        elif (startx - beginx)//32 == 2 and (starty - beginy)//32 == 3:\n",
    "            costlist[14]+=cost\n",
    "        elif (startx - beginx)//32 == 3 and (starty - beginy)//32 == 3:\n",
    "            costlist[15]+=cost\n",
    "\n",
    "    #print(costqt32)\n",
    "    #print(costqt64)\n",
    "    #print(costlist)\n",
    "    #print(costlist_32)\n",
    "    for i in range(4):\n",
    "        if (costqt32[i] + costqt64[i]) == 0:\n",
    "            continue\n",
    "        deltaRD = abs((costqt32[i] - costqt64[i]) / (costqt32[i] + costqt64[i]))\n",
    "        #print(deltaRD)\n",
    "        if deltaRD <= 0.01 :\n",
    "            #print(\"deltaRD%f\"%(deltaRD))\n",
    "            #print(\"delete\")\n",
    "            checklist64_32[i] = True\n",
    "    for i in range(16):\n",
    "        if (costlist[i] + costlist_32[i]) == 0 or (costlist[i] == costlist_32[i]) :\n",
    "            continue\n",
    "        deltaRD = abs((costlist[i] - costlist_32[i]) / (costlist[i] + costlist_32[i]))\n",
    "        if deltaRD <= 0.01 :\n",
    "            #print(\"deltaRD%f\"%(deltaRD))\n",
    "            #print(\"delete\")\n",
    "            checklist[i] = True\n",
    "    return checklist , checklist64_32"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Generate 32X32 CU depth label"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "def depth_label(test_seq,yuv_img_padding,yuv_img,CTU_index=0,cnt=0,frame_num = 0,QP = \"QP\",train=True,eliminate = True):\n",
    "    \n",
    "    org_width,org_height = yuv_img.size\n",
    "    #print(org_width,org_height )\n",
    "    width , height = yuv_img_padding.size\n",
    "    frame_padding = yuv_img_padding.convert('RGB')\n",
    "    for beginy in range(0, height, 128):\n",
    "        for beginx in range(0, width, 128):\n",
    "            #if train == True:\n",
    "            #    f = open(\"D:/frame/ratiod8/\"+test_seq+\"/\"+QP+\"/CTU_%d.txt\"%CTU_index, \"r\")\n",
    "            #else:\n",
    "            f = open(\"D:/frame/\"+test_seq+\"/\"+QP+\"/CTU_%d.txt\"%CTU_index, \"r\")\n",
    "            #print(beginy,beginx)\n",
    "            #print(CTU_index)\n",
    "            label_dict = [0]*16\n",
    "            TT_dict = [0]*16\n",
    "            for line in f:   \n",
    "                row = line.split(\" \")\n",
    "                startx,starty,depth,btdepth,mtdepth = int(row[0]),int(row[1]),int(row[4]), int(row[5]),int(row[6])\n",
    "                cuheight , cuwidth = int(row[2]) , int(row[3])\n",
    "                if startx == 0 and starty==0 and cuheight == 0 and cuwidth == 0:\n",
    "                    continue\n",
    "                if (startx - beginx)//32 == 0 and (starty - beginy)//32 == 0:\n",
    "                    #print(2*depth+btdepth)\n",
    "                    label_dict[0] = max(label_dict[0],2*depth+btdepth)\n",
    "                    TT_dict[0] = max(TT_dict[0],btdepth-mtdepth)\n",
    "                elif (startx - beginx)//32 == 1 and (starty - beginy)//32 == 0:\n",
    "                    label_dict[1] = max(label_dict[1],2*depth+btdepth)\n",
    "                    TT_dict[1] = max(TT_dict[1],btdepth-mtdepth)\n",
    "                elif (startx - beginx)//32 == 2 and (starty - beginy)//32 == 0:\n",
    "                    label_dict[2] = max(label_dict[2],2*depth+btdepth)\n",
    "                    TT_dict[2] = max(TT_dict[2],btdepth-mtdepth)\n",
    "                elif (startx - beginx)//32 == 3 and (starty - beginy)//32 == 0:\n",
    "                    label_dict[3] = max(label_dict[3],2*depth+btdepth)\n",
    "                    TT_dict[3] = max(TT_dict[3],btdepth-mtdepth)\n",
    "                elif (startx - beginx)//32 == 0 and (starty - beginy)//32 == 1:\n",
    "                    label_dict[4] = max(label_dict[4],2*depth+btdepth)\n",
    "                    TT_dict[4] = max(TT_dict[4],btdepth-mtdepth)\n",
    "                elif (startx - beginx)//32 == 1 and (starty - beginy)//32 == 1:\n",
    "                    label_dict[5] = max(label_dict[5],2*depth+btdepth)\n",
    "                    TT_dict[5] = max(TT_dict[5],btdepth-mtdepth)\n",
    "                elif (startx - beginx)//32 == 2 and (starty - beginy)//32 == 1:\n",
    "                    label_dict[6] = max(label_dict[6],2*depth+btdepth)\n",
    "                    TT_dict[6] = max(TT_dict[6],btdepth-mtdepth)\n",
    "                elif (startx - beginx)//32 == 3 and (starty - beginy)//32 == 1:\n",
    "                    label_dict[7] = max(label_dict[7],2*depth+btdepth)\n",
    "                    TT_dict[7] = max(TT_dict[7],btdepth-mtdepth)\n",
    "                elif (startx - beginx)//32 == 0 and (starty - beginy)//32 == 2:\n",
    "                    label_dict[8] = max(label_dict[8],2*depth+btdepth)\n",
    "                    TT_dict[8] = max(TT_dict[8],btdepth-mtdepth)\n",
    "                elif (startx - beginx)//32 == 1 and (starty - beginy)//32 == 2:\n",
    "                    label_dict[9] = max(label_dict[9],2*depth+btdepth)\n",
    "                    TT_dict[9] = max(TT_dict[9],btdepth-mtdepth)\n",
    "                elif (startx - beginx)//32 == 2 and (starty - beginy)//32 == 2:\n",
    "                    label_dict[10] = max(label_dict[10],2*depth+btdepth)\n",
    "                    TT_dict[10] = max(TT_dict[10],btdepth-mtdepth)\n",
    "                elif (startx - beginx)//32 == 3 and (starty - beginy)//32 == 2:\n",
    "                    label_dict[11] = max(label_dict[11],2*depth+btdepth)\n",
    "                    TT_dict[11] = max(TT_dict[11],btdepth-mtdepth)\n",
    "                elif (startx - beginx)//32 == 0 and (starty - beginy)//32 == 3:\n",
    "                    label_dict[12] = max(label_dict[12],2*depth+btdepth)\n",
    "                    TT_dict[12] = max(TT_dict[12],btdepth-mtdepth)\n",
    "                elif (startx - beginx)//32 == 1 and (starty - beginy)//32 == 3:\n",
    "                    label_dict[13] = max(label_dict[13],2*depth+btdepth)\n",
    "                    TT_dict[13] = max(TT_dict[13],btdepth-mtdepth)\n",
    "                elif (startx - beginx)//32 == 2 and (starty - beginy)//32 == 3:\n",
    "                    label_dict[14] = max(label_dict[14],2*depth+btdepth)\n",
    "                    TT_dict[14] = max(TT_dict[14],btdepth-mtdepth)\n",
    "                elif (startx - beginx)//32 == 3 and (starty - beginy)//32 == 3:\n",
    "                    label_dict[15] = max(label_dict[15],2*depth+btdepth)\n",
    "                    TT_dict[15] = max(TT_dict[15],btdepth-mtdepth)\n",
    "            #print(label_dict)\n",
    "            if train == True and eliminate == True:\n",
    "                check32,check64 = RDeliminate(test_seq,QP,ratiod8 = True,beginx=beginx , beginy=beginy , i = CTU_index)\n",
    "            \n",
    "                if label_dict[0] == 2 and check64[0] == False:\n",
    "                    label_dict[1] = 2\n",
    "                    label_dict[4] = 2\n",
    "                    label_dict[5] = 2\n",
    "                elif label_dict[0] == 2 and check64[0] == True:\n",
    "                    label_dict[1] = 0\n",
    "                    label_dict[4] = 0\n",
    "                    label_dict[5] = 0\n",
    "                if label_dict[2] == 2 and check64[1] == False:\n",
    "                    label_dict[3] = 2 \n",
    "                    label_dict[6] = 2\n",
    "                    label_dict[7] =  2\n",
    "                elif label_dict[2] == 2 and check64[1] == True:\n",
    "                    label_dict[3] = 0 \n",
    "                    label_dict[6] = 0\n",
    "                    label_dict[7] =  0\n",
    "                if label_dict[8] == 2 and check64[2] == False:\n",
    "                    label_dict[9] = 2 \n",
    "                    label_dict[12] = 2 \n",
    "                    label_dict[13] = 2\n",
    "                elif label_dict[8] == 2 and check64[2] == True:\n",
    "                    label_dict[9] = 0 \n",
    "                    label_dict[12] = 0 \n",
    "                    label_dict[13] = 0\n",
    "                if label_dict[10] == 2 and check64[3] == False:\n",
    "                    label_dict[11] = 2 \n",
    "                    label_dict[14] = 2 \n",
    "                    label_dict[15] = 2\n",
    "                elif label_dict[10] == 2 and check64[3] == True:\n",
    "                    label_dict[11] = 0 \n",
    "                    label_dict[14] = 0 \n",
    "                    label_dict[15] = 0\n",
    "            else :\n",
    "                if label_dict[0] == 2 :\n",
    "                    label_dict[1] = 2\n",
    "                    label_dict[4] = 2\n",
    "                    label_dict[5] = 2\n",
    "                    TT_dict[1] = 0\n",
    "                    TT_dict[4] = 0\n",
    "                    TT_dict[5] = 0\n",
    "                \n",
    "                if label_dict[2] == 2 :\n",
    "                    label_dict[3] = 2 \n",
    "                    label_dict[6] = 2\n",
    "                    label_dict[7] =  2\n",
    "                    TT_dict[3] = 0\n",
    "                    TT_dict[6] = 0\n",
    "                    TT_dict[7] = 0\n",
    "               \n",
    "                if label_dict[8] == 2 :\n",
    "                    label_dict[9] = 2 \n",
    "                    label_dict[12] = 2 \n",
    "                    label_dict[13] = 2\n",
    "                    TT_dict[9] = 0\n",
    "                    TT_dict[12] = 0\n",
    "                    TT_dict[13] = 0\n",
    "               \n",
    "                if label_dict[10] == 2 :\n",
    "                    label_dict[11] = 2 \n",
    "                    label_dict[14] = 2 \n",
    "                    label_dict[15] = 2\n",
    "                    TT_dict[11] = 0\n",
    "                    TT_dict[14] = 0\n",
    "                    TT_dict[15] = 0\n",
    "            #print(label_dict)\n",
    "            index = 0\n",
    "            if frame_num > 0:\n",
    "                for row_i in range(beginy, beginy+128, 32):\n",
    "                    for col_i in range(beginx, beginx+128, 32):\n",
    "                        if col_i > org_width or col_i + 32 > org_width or row_i > org_height or row_i + 32 > org_height:\n",
    "                            index +=1\n",
    "                            continue\n",
    "                        crop = frame_padding.crop((col_i, row_i, col_i + 32, row_i + 32))\n",
    "                        if train == False: \n",
    "    #                         if label_dict[index] == 4 :\n",
    "    #                             crop.save(\"D:/VCCResearch/\"+QP+\"/reimplement_val/depth4\"+\"/CU_\"+test_seq+str(cnt)+\".png\",\"PNG\")\n",
    "    #                         elif label_dict[index] == 5  :\n",
    "    #                             crop.save(\"D:/VCCResearch/\"+QP+\"/reimplement_val/depth5_6\"+\"/CU_\"+test_seq+str(cnt)+\".png\",\"PNG\")\n",
    "    #                         elif label_dict[index] == 6  :\n",
    "    #                             crop.save(\"D:/VCCResearch/\"+QP+\"/reimplement_val/depth5_6\"+\"/CU_\"+test_seq+str(cnt)+\".png\",\"PNG\")\n",
    "    #                         elif label_dict[index] == 7  :\n",
    "    #                             crop.save(\"D:/VCCResearch/\"+QP+\"/reimplement_val/depth7\"+\"/CU_\"+test_seq+str(cnt)+\".png\",\"PNG\")\n",
    "    #                         elif label_dict[index] == 8  :\n",
    "    #                             crop.save(\"D:/VCCResearch/\"+QP+\"/reimplement_val/depth8_9\"+\"/CU_\"+test_seq+str(cnt)+\".png\",\"PNG\")\n",
    "    #                         elif label_dict[index] == 9  :\n",
    "    #                             crop.save(\"D:/VCCResearch/\"+QP+\"/reimplement_val/depth8_9\"+\"/CU_\"+test_seq+str(cnt)+\".png\",\"PNG\")\n",
    "    #                         elif label_dict[index] == 10  :\n",
    "    #                             crop.save(\"D:/VCCResearch/\"+QP+\"/reimplement_val/depth10\"+\"/CU_\"+test_seq+str(cnt)+\".png\",\"PNG\")\n",
    "                            if TT_dict[index] > 0 :\n",
    "                                crop.save(\"D:/VCCResearch/\"+QP+\"/TT_CU/valid_TT/have_tt\"+\"/CU_\"+test_seq+str(cnt)+\".png\",\"PNG\")\n",
    "                            else:\n",
    "                                crop.save(\"D:/VCCResearch/\"+QP+\"/TT_CU/valid_TT/no_tt\"+\"/CU_\"+test_seq+str(cnt)+\".png\",\"PNG\")\n",
    "                        elif  train == True and eliminate == True:\n",
    "\n",
    "                                #if label_dict[index] == 4  and check32[index] == False:\n",
    "                                #   crop.save(\"D:/VCCResearch/\"+QP+\"_5class/train_depthall/depth4\"+\"/CU_\"+test_seq+str(cnt)+\".png\",\"PNG\")\n",
    "                            if label_dict[index] == 5 and check32[index] == False:\n",
    "                                crop.save(\"D:/VCCResearch/\"+QP+\"/train_depthall/depth5_6\"+\"/CU_\"+test_seq+str(cnt)+\".png\",\"PNG\")\n",
    "                            elif label_dict[index] == 6 and check32[index] == False:\n",
    "                                crop.save(\"D:/VCCResearch/\"+QP+\"/train_depthall/depth5_6\"+\"/CU_\"+test_seq+str(cnt)+\".png\",\"PNG\")\n",
    "                            elif label_dict[index] == 7 and check32[index] == False:\n",
    "                                crop.save(\"D:/VCCResearch/\"+QP+\"/train_depthall/depth7_8\"+\"/CU_\"+test_seq+str(cnt)+\".png\",\"PNG\")\n",
    "                            elif label_dict[index] == 8 and check32[index] == False:\n",
    "                                crop.save(\"D:/VCCResearch/\"+QP+\"/train_depthall/depth7_8\"+\"/CU_\"+test_seq+str(cnt)+\".png\",\"PNG\")\n",
    "                            elif label_dict[index] == 9 and check32[index] == False:\n",
    "                                crop.save(\"D:/VCCResearch/\"+QP+\"/train_depthall/depth9_10\"+\"/CU_\"+test_seq+str(cnt)+\".png\",\"PNG\")\n",
    "                            elif label_dict[index] == 10 and check32[index] == False:\n",
    "                                crop.save(\"D:/VCCResearch/\"+QP+\"/train_depthall/depth9_10\"+\"/CU_\"+test_seq+str(cnt)+\".png\",\"PNG\")\n",
    "\n",
    "                        elif  train == True and eliminate == False:\n",
    "                            if TT_dict[index] > 0 :\n",
    "                                crop.save(\"D:/VCCResearch/\"+QP+\"/TT_CU/train_TT/have_tt\"+\"/CU_\"+test_seq+str(cnt)+\".png\",\"PNG\")\n",
    "                            else:\n",
    "                                crop.save(\"D:/VCCResearch/\"+QP+\"/TT_CU/train_TT/no_tt\"+\"/CU_\"+test_seq+str(cnt)+\".png\",\"PNG\")\n",
    "    #                         if label_dict[index] == 4  :\n",
    "    #                             crop.save(\"D:/VCCResearch/\"+QP+\"/reimplement_train/depth4\"+\"/CU_\"+test_seq+str(cnt)+\".png\",\"PNG\")\n",
    "    #                         elif label_dict[index] == 5 :\n",
    "    #                             crop.save(\"D:/VCCResearch/\"+QP+\"/reimplement_train/depth5_6\"+\"/CU_\"+test_seq+str(cnt)+\".png\",\"PNG\")\n",
    "    #                         elif label_dict[index] == 6 :\n",
    "    #                             crop.save(\"D:/VCCResearch/\"+QP+\"/reimplement_train/depth5_6\"+\"/CU_\"+test_seq+str(cnt)+\".png\",\"PNG\")\n",
    "    #                         elif label_dict[index] == 7 :\n",
    "    #                             crop.save(\"D:/VCCResearch/\"+QP+\"/reimplement_train/depth7\"+\"/CU_\"+test_seq+str(cnt)+\".png\",\"PNG\")\n",
    "    #                         elif label_dict[index] == 8 :\n",
    "    #                             crop.save(\"D:/VCCResearch/\"+QP+\"/reimplement_train/depth8_9\"+\"/CU_\"+test_seq+str(cnt)+\".png\",\"PNG\")\n",
    "    #                         elif label_dict[index] == 9 :\n",
    "    #                             crop.save(\"D:/VCCResearch/\"+QP+\"/reimplement_train/depth8_9\"+\"/CU_\"+test_seq+str(cnt)+\".png\",\"PNG\")\n",
    "    #                         elif label_dict[index] == 10 :\n",
    "    #                             crop.save(\"D:/VCCResearch/\"+QP+\"/reimplement_train/depth10\"+\"/CU_\"+test_seq+str(cnt)+\".png\",\"PNG\")\n",
    "                        cnt+=1\n",
    "                        index +=1\n",
    "            \n",
    "            CTU_index+=1\n",
    "            #print(CTU_index)\n",
    "    return CTU_index,cnt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Tango2\n",
      "0 510 0\n",
      "1 1020 8040\n",
      "2 1530 16080\n",
      "Finish\n",
      "FoodMarket4\n",
      "0 510 0\n",
      "1 1020 8040\n",
      "2 1530 16080\n",
      "Finish\n",
      "Campfire\n",
      "0 510 0\n",
      "1 1020 8040\n",
      "2 1530 16080\n",
      "Finish\n",
      "CatRobot1\n",
      "0 510 0\n",
      "1 1020 8040\n",
      "2 1530 16080\n",
      "Finish\n",
      "DaylightRoad2\n",
      "0 510 0\n",
      "1 1020 8040\n",
      "2 1530 16080\n",
      "Finish\n",
      "ParkRunning3\n",
      "0 510 0\n",
      "1 1020 8040\n",
      "2 1530 16080\n",
      "Finish\n",
      "MarketPlace\n",
      "0 135 0\n",
      "1 270 1980\n",
      "2 405 3960\n",
      "Finish\n",
      "RitualDance\n",
      "0 135 0\n",
      "1 270 1980\n",
      "2 405 3960\n",
      "Finish\n",
      "Cactus\n",
      "0 135 0\n",
      "1 270 1980\n",
      "2 405 3960\n",
      "Finish\n",
      "BasketballDrive\n",
      "0 135 0\n",
      "1 270 1980\n",
      "2 405 3960\n",
      "Finish\n",
      "BQTerrace\n",
      "0 135 0\n",
      "1 270 1980\n",
      "2 405 3960\n",
      "Finish\n",
      "RaceHorsesC\n",
      "0 28 0\n",
      "1 56 390\n",
      "2 84 780\n",
      "Finish\n",
      "BQMall\n",
      "0 28 0\n",
      "1 56 390\n",
      "2 84 780\n",
      "Finish\n",
      "PartyScene\n",
      "0 28 0\n",
      "1 56 390\n",
      "2 84 780\n",
      "Finish\n",
      "BasketballDrill\n",
      "0 28 0\n",
      "1 56 390\n",
      "2 84 780\n",
      "Finish\n",
      "RaceHorses\n",
      "0 8 0\n",
      "1 16 91\n",
      "2 24 182\n",
      "Finish\n",
      "BQSquare\n",
      "0 8 0\n",
      "1 16 91\n",
      "2 24 182\n",
      "Finish\n",
      "BlowingBubbles\n",
      "0 8 0\n",
      "1 16 91\n",
      "2 24 182\n",
      "Finish\n",
      "BasketballPass\n",
      "0 8 0\n",
      "1 16 91\n",
      "2 24 182\n",
      "Finish\n",
      "FourPeople\n",
      "0 60 0\n",
      "1 120 880\n",
      "2 180 1760\n",
      "Finish\n",
      "Johnny\n",
      "0 60 0\n",
      "1 120 880\n",
      "2 180 1760\n",
      "Finish\n",
      "KristenAndSara\n",
      "0 60 0\n",
      "1 120 880\n",
      "2 180 1760\n",
      "Finish\n",
      "Tango2\n",
      "0 510 0\n",
      "1 1020 8040\n",
      "2 1530 16080\n",
      "Finish\n",
      "FoodMarket4\n",
      "0 510 0\n",
      "1 1020 8040\n",
      "2 1530 16080\n",
      "Finish\n",
      "Campfire\n",
      "0 510 0\n",
      "1 1020 8040\n",
      "2 1530 16080\n",
      "Finish\n",
      "CatRobot1\n",
      "0 510 0\n",
      "1 1020 8040\n",
      "2 1530 16080\n",
      "Finish\n",
      "DaylightRoad2\n",
      "0 510 0\n",
      "1 1020 8040\n",
      "2 1530 16080\n",
      "Finish\n",
      "ParkRunning3\n",
      "0 510 0\n",
      "1 1020 8040\n",
      "2 1530 16080\n",
      "Finish\n",
      "MarketPlace\n",
      "0 135 0\n",
      "1 270 1980\n",
      "2 405 3960\n",
      "Finish\n",
      "RitualDance\n",
      "0 135 0\n",
      "1 270 1980\n",
      "2 405 3960\n",
      "Finish\n",
      "Cactus\n",
      "0 135 0\n",
      "1 270 1980\n",
      "2 405 3960\n",
      "Finish\n",
      "BasketballDrive\n",
      "0 135 0\n",
      "1 270 1980\n",
      "2 405 3960\n",
      "Finish\n",
      "BQTerrace\n",
      "0 135 0\n",
      "1 270 1980\n",
      "2 405 3960\n",
      "Finish\n",
      "RaceHorsesC\n",
      "0 28 0\n",
      "1 56 390\n",
      "2 84 780\n",
      "Finish\n",
      "BQMall\n",
      "0 28 0\n",
      "1 56 390\n",
      "2 84 780\n",
      "Finish\n",
      "PartyScene\n",
      "0 28 0\n",
      "1 56 390\n",
      "2 84 780\n",
      "Finish\n",
      "BasketballDrill\n",
      "0 28 0\n",
      "1 56 390\n",
      "2 84 780\n",
      "Finish\n",
      "RaceHorses\n",
      "0 8 0\n",
      "1 16 91\n",
      "2 24 182\n",
      "Finish\n",
      "BQSquare\n",
      "0 8 0\n",
      "1 16 91\n",
      "2 24 182\n",
      "Finish\n",
      "BlowingBubbles\n",
      "0 8 0\n",
      "1 16 91\n",
      "2 24 182\n",
      "Finish\n",
      "BasketballPass\n",
      "0 8 0\n",
      "1 16 91\n",
      "2 24 182\n",
      "Finish\n",
      "FourPeople\n",
      "0 60 0\n",
      "1 120 880\n",
      "2 180 1760\n",
      "Finish\n",
      "Johnny\n",
      "0 60 0\n",
      "1 120 880\n",
      "2 180 1760\n",
      "Finish\n",
      "KristenAndSara\n",
      "0 60 0\n",
      "1 120 880\n",
      "2 180 1760\n",
      "Finish\n",
      "Tango2\n",
      "0 510 0\n",
      "1 1020 8040\n",
      "2 1530 16080\n",
      "Finish\n",
      "FoodMarket4\n",
      "0 510 0\n",
      "1 1020 8040\n",
      "2 1530 16080\n",
      "Finish\n",
      "Campfire\n",
      "0 510 0\n",
      "1 1020 8040\n",
      "2 1530 16080\n",
      "Finish\n",
      "CatRobot1\n",
      "0 510 0\n",
      "1 1020 8040\n",
      "2 1530 16080\n",
      "Finish\n",
      "DaylightRoad2\n",
      "0 510 0\n",
      "1 1020 8040\n",
      "2 1530 16080\n",
      "Finish\n",
      "ParkRunning3\n",
      "0 510 0\n",
      "1 1020 8040\n",
      "2 1530 16080\n",
      "Finish\n",
      "MarketPlace\n",
      "0 135 0\n",
      "1 270 1980\n",
      "2 405 3960\n",
      "Finish\n",
      "RitualDance\n",
      "0 135 0\n",
      "1 270 1980\n",
      "2 405 3960\n",
      "Finish\n",
      "Cactus\n",
      "0 135 0\n",
      "1 270 1980\n",
      "2 405 3960\n",
      "Finish\n",
      "BasketballDrive\n",
      "0 135 0\n",
      "1 270 1980\n",
      "2 405 3960\n",
      "Finish\n",
      "BQTerrace\n",
      "0 135 0\n",
      "1 270 1980\n",
      "2 405 3960\n",
      "Finish\n",
      "RaceHorsesC\n",
      "0 28 0\n",
      "1 56 390\n",
      "2 84 780\n",
      "Finish\n",
      "BQMall\n",
      "0 28 0\n",
      "1 56 390\n",
      "2 84 780\n",
      "Finish\n",
      "PartyScene\n",
      "0 28 0\n",
      "1 56 390\n",
      "2 84 780\n",
      "Finish\n",
      "BasketballDrill\n",
      "0 28 0\n",
      "1 56 390\n",
      "2 84 780\n",
      "Finish\n",
      "RaceHorses\n",
      "0 8 0\n",
      "1 16 91\n",
      "2 24 182\n",
      "Finish\n",
      "BQSquare\n",
      "0 8 0\n",
      "1 16 91\n",
      "2 24 182\n",
      "Finish\n",
      "BlowingBubbles\n",
      "0 8 0\n",
      "1 16 91\n",
      "2 24 182\n",
      "Finish\n",
      "BasketballPass\n",
      "0 8 0\n",
      "1 16 91\n",
      "2 24 182\n",
      "Finish\n",
      "FourPeople\n",
      "0 60 0\n",
      "1 120 880\n",
      "2 180 1760\n",
      "Finish\n",
      "Johnny\n",
      "0 60 0\n",
      "1 120 880\n",
      "2 180 1760\n",
      "Finish\n",
      "KristenAndSara\n",
      "0 60 0\n",
      "1 120 880\n",
      "2 180 1760\n",
      "Finish\n",
      "Tango2\n",
      "0 510 0\n",
      "1 1020 8040\n",
      "2 1530 16080\n",
      "Finish\n",
      "FoodMarket4\n",
      "0 510 0\n",
      "1 1020 8040\n",
      "2 1530 16080\n",
      "Finish\n",
      "Campfire\n",
      "0 510 0\n",
      "1 1020 8040\n",
      "2 1530 16080\n",
      "Finish\n",
      "CatRobot1\n",
      "0 510 0\n",
      "1 1020 8040\n",
      "2 1530 16080\n",
      "Finish\n",
      "DaylightRoad2\n",
      "0 510 0\n",
      "1 1020 8040\n",
      "2 1530 16080\n",
      "Finish\n",
      "ParkRunning3\n",
      "0 510 0\n",
      "1 1020 8040\n",
      "2 1530 16080\n",
      "Finish\n",
      "MarketPlace\n",
      "0 135 0\n",
      "1 270 1980\n",
      "2 405 3960\n",
      "Finish\n",
      "RitualDance\n",
      "0 135 0\n",
      "1 270 1980\n",
      "2 405 3960\n",
      "Finish\n",
      "Cactus\n",
      "0 135 0\n",
      "1 270 1980\n",
      "2 405 3960\n",
      "Finish\n",
      "BasketballDrive\n",
      "0 135 0\n",
      "1 270 1980\n",
      "2 405 3960\n",
      "Finish\n",
      "BQTerrace\n",
      "0 135 0\n",
      "1 270 1980\n",
      "2 405 3960\n",
      "Finish\n",
      "RaceHorsesC\n",
      "0 28 0\n",
      "1 56 390\n",
      "2 84 780\n",
      "Finish\n",
      "BQMall\n",
      "0 28 0\n",
      "1 56 390\n",
      "2 84 780\n",
      "Finish\n",
      "PartyScene\n",
      "0 28 0\n",
      "1 56 390\n",
      "2 84 780\n",
      "Finish\n",
      "BasketballDrill\n",
      "0 28 0\n",
      "1 56 390\n",
      "2 84 780\n",
      "Finish\n",
      "RaceHorses\n",
      "0 8 0\n",
      "1 16 91\n",
      "2 24 182\n",
      "Finish\n",
      "BQSquare\n",
      "0 8 0\n",
      "1 16 91\n",
      "2 24 182\n",
      "Finish\n",
      "BlowingBubbles\n",
      "0 8 0\n",
      "1 16 91\n",
      "2 24 182\n",
      "Finish\n",
      "BasketballPass\n",
      "0 8 0\n",
      "1 16 91\n",
      "2 24 182\n",
      "Finish\n",
      "FourPeople\n",
      "0 60 0\n",
      "1 120 880\n",
      "2 180 1760\n",
      "Finish\n",
      "Johnny\n",
      "0 60 0\n",
      "1 120 880\n",
      "2 180 1760\n",
      "Finish\n",
      "KristenAndSara\n",
      "0 60 0\n",
      "1 120 880\n",
      "2 180 1760\n",
      "Finish\n"
     ]
    }
   ],
   "source": [
    "#CTU_index,cnt = 0,0\n",
    "#QP = \"QP22\"\n",
    "QPList = [\"QP22\",\"QP27\",\"QP32\",\"QP37\"]\n",
    "#QPList = [\"QP22\"]\n",
    "#seq_list = [\"BasketballPass\"]\n",
    "seq_list = [\"Tango2\",\"FoodMarket4\",\"Campfire\",\"CatRobot1\",\"DaylightRoad2\",\"ParkRunning3\",\"MarketPlace\",\"RitualDance\",\n",
    "            \"Cactus\",\"BasketballDrive\",\"BQTerrace\",\"RaceHorsesC\",\"BQMall\",\"PartyScene\",\"BasketballDrill\",\"RaceHorses\",\n",
    "            \"BQSquare\",\"BlowingBubbles\",\"BasketballPass\",\"FourPeople\",\"Johnny\",\"KristenAndSara\"\n",
    "            ]\n",
    "#seq_list = {\"intra_train_1\":60,\"intra_train_2\":40,\"intra_train_3\":10,\"intra_train_4\":10}\n",
    "for QP in QPList:\n",
    "    for seq_name in seq_list:\n",
    "        CTU_index,cnt = 0,0\n",
    "        print(seq_name)\n",
    "        for i in range(3):\n",
    "            img = frame_processing(seq_name,currframe = i)\n",
    "            yuv_img = Image.fromarray(img.astype('uint8')).convert('YCbCr')\n",
    "            yuv_img_padding = frame_padding(img)\n",
    "\n",
    "            yuv_img_padding = Image.fromarray(yuv_img_padding.astype('uint8')).convert('YCbCr')\n",
    "            CTU_index,cnt = depth_label(seq_name,yuv_img_padding,yuv_img, CTU_index , cnt , i ,QP,False,False)\n",
    "            print(i,CTU_index,cnt)\n",
    "        print(\"Finish\") \n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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